# List of Book Chapters

## Wolfgang Banzhaf

Older papers (from 1993 back) are represented by abstracts only and are available upon email request
We give titles and links. If you click the underlined words in a title you will see an abstract and source information of the paper. If you click the corresponding filename you will retrieve a copy.

# List of Abstracts and Sources

#### SOURCE: Evolution in Action - Past, Present and Future: A Festschrift for Erik Goodman W. Banzhaf, B.H.C. Cheng, K. Deb, K.E. Holekamp, R.E. Lenski, C. Ofria, R.T. Pennock, W.F. Punch, D.J. Whittaker (Eds.), Springer, Cham, Switzerland, 2020, pp. 385 - 407

ABSTRACT of Book Chapter: The automation of program repair can be coached in terms of search algorithms. Repair templates derived from common bug-fix patterns can be used to determine a promising search space with potentially many correct patches, a space that can be effectively explored by GP methods. Here we propose a new repair system, ARJA-p, extended from our earlier ARJA system of bug repair for JAVA, which integrates and enhances the performance of the first approach that combines repair templates and EC, PAR. Empirical results on 224 real bugs in Defects4J show that ARJA-p outperforms state-of-the-art repair approaches by a large margin, both in terms of the number of bugs fixed and of their correctness. Specifically, ARJA-p can increase the number of fixed bugs in Defects4J by 29.2% (from 65 to 84) and the number of correctly fixed bugs by 42.3% (from 26 to 37).

#### SOURCE: Genetic Programming - Theory and Practice XVII W. Banzhaf, E. Goodman, L. Sheneman, L. Trujillo, B. Worzel (Eds.), Springer, Cham, Switzerland, 2020, pp. 101 - 119

ABSTRACT of Book Chapter: Video games provide a well-defined study ground for the development of behavioural agents that learn through trial-and-error interaction with their environment, or reinforcement learning (RL). They cover a diverse range of environments that are designed to be challenging for humans, all through a high-dimensional visual interface. Tangled Program Graphs (TPG) is a recently proposed genetic programming algorithm that emphasizes emergent modularity (i.e. automatic construction of multi-agent organisms) in order to build successful RL agents more efficiently than state-of-the-art solutions from other sub-fields of artificial intelligence, e.g. deep neural networks. However, TPG organisms represent a direct mapping from input to output with no mechanism to integrate past experience (previous inputs). This is a limitation in environments with partial observability. For example, TPG performed poorly in video games that explicitly require the player to predict the trajectory of a moving object. In order to make these calculations, players must identify, store, and reuse important parts of past experience. In this work, we describe an approach to supporting this type of short-term temporal memory in TPG, and show that shared memory among subsets of agents within the same organism seems particularly important. In addition, we introduce heterogeneous TPG organisms composed of agents with distinct types of representation that collaborate through shared memory. In this study, heterogeneous organisms provide a parsimonious approach to supporting agents with task-specific functionality, image processing capabilities in the case of this work. Taken together, these extensions allow TPG to discover high-scoring behaviours for the Atari game Breakout, which is an environment it failed to make significant progress on previously.

#### SOURCE: Genetic Programming - Theory and Practice XVII W. Banzhaf, E. Goodman, L. Sheneman, L. Trujillo, B. Worzel (Eds.), Springer, Cham, Switzerland, 2020, pp. 25 - 38

ABSTRACT of Book Chapter: The present and future of evolutionary algorithms depends on the proper use of modern parallel and distributed computing infrastructures. Although still sequential approaches dominate the landscape, available multi-core, many-core and distributed systems will make users and researchers to more frequently deploy parallel version of the algorithms. In such a scenario, new possibilities arise regarding the time saved when parallel evaluation of individuals are performed. And this time saving is particularly relevant in Genetic Programming. This paper studies how evaluation time influences not only time to solution in parallel/distributed systems, but may also affect size evolution of individuals in the population, and eventually will reduce the bloat phenomenon GP features. This paper considers time and space as two sides of a single coin when devising a more natural method for fighting bloat. This new perspective allows us to understand that new methods for bloat control can be derived, and the first of such a method is described and tested. Experimental data confirms the strength of the approach: using computing time as a measure of individuals’ complexity allows to control the growth in size of genetic programming individuals.

#### SOURCE: Genetic Programming - Theory and Practice XVII W. Banzhaf, E. Goodman, L. Sheneman, L. Trujillo, B. Worzel (Eds.), Springer, Cham, Switzerland, 2020, pp. 367 - 381

ABSTRACT of Book Chapter: This chapter proposes a new model of tree-based Genetic Programming (GP) as a simple sampling algorithm that samples minimal schemata (subsets of the solution space) described by a single concrete node at a single position in the expression tree. We show that GP explores these schemata in the same way across three benchmarks, rapidly converging the population to a specific function at each position throughout the upper layers of the expression tree. This convergence is driven by covariance between membership of a simple schema and rank fitness. We model this process using Price’s theorem and provide empirical evidence to support our model. The chapter closes with an outline of a modification of the standard GP algorithm that reinforces this bias by converging populations to fit schemata in an accelerated way.

#### SOURCE: Genetic Programming - Theory and Practice XV W. Banzhaf, R. Olson, W. Tozier, R. Riolo (Eds.), Springer, Cham, Switzerland, 2018, pp. 73 - 83

ABSTRACT of Book Chapter: In natural ecologies, niches are created, altered, or destroyed, driving populations to continually change and produce novel features. Here, we explore an approach to guiding evolution via the power of niches: ecologically-mediated hints. The original exploration of ecologically-mediated hints occurred in Eco-EA, an algorithm in which an experimenter provides a primary fitness function for a tough problem that they are trying to solve, as well as “hints” that are associated with limited resources. We hypothesize that other evolutionary algorithms that create niches, such as lexicase selection, can be provided hints in a similar way. Here, we use a toy problem to investigate the expected benefits of using this approach to solve more challenging problems. Of course, since humans are notoriously bad at choosing fitness functions, user-provided advice may be misleading. Thus, we also explore the impact of misleading hints. As expected, we find that informative hints facilitate solving the problem. However, the mechanism of niche-creation (Eco-EA vs. lexicase selection) dramatically impacts the algorithm’s robustness to misleading hints.

#### SOURCE: Inspired by Nature S. Stepney, A. Adamatzky (Eds.), Springer, Cham, Switzerland, 2018, pp. 145 - 156

ABSTRACT of Book Chapter: In this chapter we take a fresh look at the current status of evolving computer code using Genetic Programming methods. The emphasis is not so much on what has been achieved in detail in the past few years, but on the general research direction of code evolution and its ramifications for GP. We begin with a quick glance at the area of Search-based Software Engineering (SBSE), discuss the history of GP as applied to code evolution, consider various application scenarios, and speculate on techniques that might lead to a scaling-up of present-day approaches.

#### SOURCE: From Astrophysics to Unconventional Computation A. Adamatzky, V. Kendon (Eds.), Springer, Cham, Switzerland, 2020, pp. 1 - 21

ABSTRACT of Book Chapter: This contribution argues that the notion of time used in the scientific modeling of reality deprives time of its real nature. Difficulties from logic paradoxes to mathematical incompleteness and numerical uncertainty ensue. How can the emergence of novelty in the Universe be explained? How can the creativity of the evolutionary process leading to ever more complex forms of life be captured in our models of reality? These questions are deeply related to our understanding of time. We argue here for a computational framework of modeling that seems to us the only currently known type of modeling available in Science able to capture aspects of the nature of time required to better model and understand real phenomena.

#### SOURCE: Evolution in Action - Past, Present and Future: A Festschrift for Erik Goodman W. Banzhaf, B.H.C. Cheng, K. Deb, K.E. Holekamp, R.E. Lenski, C. Ofria, R.T. Pennock, W.F. Punch, D.J. Whittaker (Eds.), Springer, Cham, Switzerland, 2020, pp. 467 - 487

ABSTRACT of Book Chapter: The use of genetic variation (DNA markers) has become widespread for prediction of genetic merit in animal and plant breeding and it is gaining momentum as a prognostic tool for propensity to disease in human medicine. Although conceptually straightforward, genomic prediction is a very challenging problem. Genotyping organisms and recording phenotypic traits are time consuming and expensive. Resultant datasets often have many more features (markers) than samples (organisms). Therefore, models attempting to estimate the effects of markers often suffer from overfitting due to the curse of dimensionality. Feature selection is desirable in this setting to remove markers that do not appreciably affect the trait being predicted and amount to statistical noise.We present a differential evolution system for feature selection in genomic prediction problems and demonstrate its performance on simulated data. Code is available at: https://github.com/ianwhale/tblup.

#### SOURCE: Genetic Programming - Theory and Practice XIV R. Riolo, B. Goldman, B. Tozier,W.P. Worzel (Eds.), Springer, New York, 2017, pp.

ABSTRACT of Book Chapter: Redundant mapping from genotype to phenotype is common in evolutionary algorithms, especially in genetic programming (GP). Such a redundancy leads to neutrality, a situation where mutations to a genotype may not alter its phenotypic outcome. The effects of neutrality can be better understood by quantitatively analyzing its two observed properties, i.e., robustness and evolvability. In this chapter, we summarize our previous work on this topic in examining a compact Linear GP algorithm. Due to the choice of this particular system we can characterize its entire genotype, phenotype, and fitness networks, and quantitatively measure robustness and evolvability at the genotypic, phenotypic, and fitness levels. We then investigate the relationship between robustness and evolvability at those different levels. Technically, we use an ensemble of random walkers and hill climbers to study how robustness and evolvability are related to the structure of genotypic, phenotypic, and fitness networks and influence the evolutionary search process.

#### SOURCE: Transactions on Rough Sets XX Peters, J.F., Skowron, A. (Eds.), Springer LNCS Vol. 10020, Berlin, 2016, 1-23

ABSTRACT of Book Chapter: Feature selecting is considered as one of the most important pre-process methods in machine learning, data mining and bioinformatics. By applying pre-process techniques, we can defy the curse of dimensionality by reducing computational and storage costs, facilitate data understanding and visualization, and diminish training and testing times, leading to overall performance improvement, especially when dealing with large datasets. Correlation feature selection method uses a conventional merit to evaluate different feature subsets. In this paper, we propose a new merit by adapting and employing of correlation feature selection in conjunction with fuzzy-rough feature selection, to improve the effectiveness and quality of the conventional methods. It also outperforms the newly introduced gradient boosted feature selection, by selecting more relevant and less redundant features. The two-step experimental results show the applicability and efficiency of our proposed method over some well known and mostly used datasets, as well as newly introduced ones, especially from the UCI collection with various sizes from small to large numbers of features and samples.

#### SOURCE: Opinions and Outlooks on Morphological Computation H. Hauser, R.M. Fuechslin and R. Pfeifer (Eds.), Electronic Publishing, 2014, Chapter 16

ABSTRACT of Book Chapter: In this contribution a broad perspective on morphological computation is introduced. We shall argue that shape is a special feature of spatial relations and shall advocate a computation model that takes into account spatial relationships between components of the computation.

#### SOURCE: Genetic Programming - Theory and Practice XIII R. Riolo, W.P. Worzel, M. Kotanchek, A. Kordon (Eds.), Springer, New York, 2016, 39 - 57

ABSTRACT of Book Chapter: A data set for classification is commonly composed of a set of features defining the data space representation and one attribute corresponding to the instances’ class. A classification tool has to discover how to separate classes based on features, but the discovery of useful knowledge may be hampered by inadequate or insufficient features. Pre-processing steps for the automatic construction of new high-level features proposed to discover hidden relationships among features and to improve classification quality. Here we present a new tool for highlevel feature construction: Kaizen Programming. This tool can construct many complementary/dependent high-level features simultaneously. We show that our approach outperforms related methods on well-known binary-class medical data sets using a decision-tree classifier, achieving greater accuracy and smaller trees.

#### SOURCE: Morphogenetic Engineering R. Doursat, H. Sayama, O. Michel (Eds.), Springer, Heidelberg, 2012, 331 - 351

ABSTRACT of Book Chapter: We argue that artificial development is an appropriate means of approaching complex systems engineering. Artificial development works via the inclusion of mechanisms that enhance the evolvability of a design space. Two of these mecha- nisms, regularities and adaptive feedback with the environment, are discussed. We concentrate on the less explored of the two: adaptive feedback. A concrete example is presented and applied to a simple artificial problem resembling vasculogenesis. It is shown that the use of a local feedback function substantively improves the efficacy of a machine learner on the problem. Further, inclusion of this adaptive feedback eliminates the sensitivity of the machine learner to a system parameter previously shown to correspond to problem hardness.

#### SOURCE: Parallel Architectures and Bioinspired Algorithms F. Fernandez de Vega, J.I. Hidalgo Perez and J. Lanchares (Eds.), Computational Intelligence Series, Springer, Heidelberg, 2012, pp. 51 - 76

ABSTRACT of Book Chapter: Optimized shape design is used for such applications as wing de- sign in aircraft, hull design in ships, and more generally rotor optimization in turbomachinery such as that of aircraft, ships, and wind turbines. We present work on optimized shape design using a technique from the area of Genetic Programming, self-modifying Cartesian Genetic Programming (SMCGP), to evolve shapes with specific criteria, such as minimized drag or maximized lift. This technique is well suited for a distributed parallel system to increase efficiency. Fitness evaluation of the genetic programming technique is accomplished through a custom implementation of a fluid dynamics solver running on graphics processing units (GPUs). Solving fluid dynamics systems is a computationally expensive task and requires optimization in order for the evolution to complete in a practical period of time. In this chapter, we shall describe both the SMCGP technique and the GPU fluid dynamics solver that together provide a robust and efficient shape design system.

#### SOURCE: Engineered Biomimicry A. Lakhtakia and R.J. Martín-Palma (Eds.), Elsevier, Waltham, MA, 2013, 429 - 447

ABSTRACT of Book Chapter: This chapter focuses on evolutionary computation, in particular genetic programming, as examples of drawing inspiration from biological systems. We set the choice of evolution as a source for inspiration in context and discuss the history of evolutionary computation and its variants before looking more closely at genetic programming. After a discussion of methods and the state of the art, we review application areas of genetic programming and its strength in providing human-competitive solutions.

#### SOURCE: Massively Parallel Evolutionary Computation on GPGPUs S. Tsutsui and P. Collet (Eds.), Springer, New York, Natural Computing Series, 2013, 389 - 419

ABSTRACT: An Artificial Chemistry is an abstract model of a chemistry that can be used to model real chemical and biological processes, as well as any natural or artificial phenomena involving interactions among objects and their transformations. It can also be used to perform computations inspired by chemistry, including heuristic optimization algorithms akin to evolutionary algorithms, among other usages. Artificial chemistries are conceptually parallel computations, and could greatly benefit from parallel computer architectures for their simulation, especially as GPU hardware becomes widespread and affordable. However, in practice it is difficult to parallelize artificial chemistry algorithms efficiently for GPUs, particularly in the case of stochastic simulation algorithms that model individual molecular collisions and take chemical kinetics into account. This chapter surveys the current state of the art in the techniques for parallelizing artificial chemistries on GPUs, with focus on their stochastic simulation and their applications in the evolutionary computation domain. Since this problem is far from being entirely solved to satisfaction, some suggestions for future research are also outlined.

#### SOURCE: Cartesian Genetic Programming J. Miller (Ed.), Springer, New York, CI Series, 2011, 101 - 124

ABSTRACT: Self-modifying Cartesian genetic programming (SMCGP) is a general purpose, graph-based, form of genetic programming founded on Cartesian genetic programming. In addition to the usual computational functions, it includes functions that can modify the program encoded in the genotype. SMCGP has high scalability in that evolved programs encoded in the genotype can be iterated to produce an infinite sequence of programs (phenotypes). It also allows programs to acquire more inputs and produce more outputs during iterations. Another attractive feature of SMCGP is that it facilitates the evolution of provably general solutions to various computational problems.

#### SOURCE: Cartesian Genetic Programming J. Miller (Ed.), Springer, New York, CI Series, 2011, 181 - 215

ABSTRACT: n this chapter, we will present three applications in which CGP can automatically generate novel image processing algorithms that compare to or exceed the best known conventional solutions. The applications fall into the areas of image preprocessing and classification.

#### SOURCE: Cartesian Genetic Programming J. Miller (Ed.), Springer, New York, CI Series, 2011, 231 - 253

ABSTRACT: As with other forms of genetic programming, evaluation of the fitness function in CGP is a major bottleneck. Recently there has been a lot of interest in exploiting the parallel processing capabilities of the Graphics Processing Units that are found on modern graphics cards. Using these processors it is possible to greatly accelerate evaluation of CGP individuals.

#### SOURCE: Knowledge Mining Using Intelligent Agents S. Dehuri, S.-B. Cho (Eds.), Imperial College Press, London, 2010, 27 - 60

ABSTRACT: This chapter discusses the use of evolutionary computation in data min- ing and knowledge discovery by using intrusion detection systems as an example. The discussion centers around the role of EAs in achieving the two high-level primary goals of data mining: prediction and descrip- tion. In particular, classi¯cation and regression tasks for prediction, and clustering tasks for description. The use of EAs for feature selection in the pre-processing step is also discussed. Another goal of this chapter was to show how basic elements in EAs, such as representations, selec- tion schemes, evolutionary operators, and ¯tness functions have to be adapted to extract accurate and useful patterns from data in di®erent data mining tasks.

#### SOURCE: Natural Computing in Computational Finance A. Brabazon, M. O'Neill, D. Maringer (Eds.), Springer, New York, CI Series, 2010, 191 - 212

ABSTRACT: A developmental co-evolutionary genetic programming approach (PAM DGP) is compared to a standard linear genetic programming (LGP) implementation for trading of stocks in the technology sector. Both interday and intraday data for these stocks were analyzed, where both implementations were found to be impressively robust to market fluctuations while reacting efficiently to opportunities for profit. PAM DGP proved slightly more reactive to market changes compared to LGP for intraday data, where the converse held true for interday data. Both implementations had very impressive accuracy in choosing both profitable buy trades and sells that prevented losses for both interday and intraday stock data. These successful trades occurred in the context of moderately active trading for interday prices and lower levels of trading for intraday prices.

#### SOURCE: Genetic Programming Theory and Practice VIII R. Riolo, T. McConaghy, E. Vladislavleva (Eds.), Springer, New York, GEC Series, 2011, 91 - 107

ABSTRACT: Self-Modifying Cartesian Genetic Programming (SMCGP) is a general purpose, graph-based, developmental form of Cartesian Genetic Programming. In addition to the usual computational functions found in CGP, SMCGP includes functions that can modify the evolved program at run time. This means that programs can be iterated to produce an infinite sequence of phenotypes from a single evolved genotype. Here, we discuss the results of using SMCGP on a variety of different problems, and see that SMCGP is able to solve tasks that require scalability and plasticity. We demonstrate how SMCGP is able to produce results that would be impossible for conventional, static Genetic Programming techniques.

#### SOURCE: Genetic Programming Theory and Practice VII R. Riolo, U.-M. O'Reilly and T. McConaghy (Eds.), Springer, New York, GEC Series, 2010, 119 - 134

ABSTRACT: A developmental co-evolutionary genetic programming approach (PAM DGP) and a standard linear genetic programming (LGP) stock trading systemare applied to a number of stocks across market sectors. Both GP techniques were found to be robust to market fluctuations and reactive to opportunities associated with stock price rise and fall, with PAMDGP generating notably greater profit in some stock trend scenarios. Both algorithms were very accurate at buying to achieve profit and selling to protect assets, while exhibiting bothmoderate trading activity and the ability to maximize or minimize investment as appropriate. The content of the trading rules produced by both algorithms are also examined in relation to stock price trend scenarios.

#### SOURCE: Genetic Programming Theory and Practice VI R. Riolo, T. Soule and B. Worzel (Eds.), Springer, New York, GEC Series, 2009, 229 - 248

ABSTRACT: Graphics Processing Units (GPUs) are in the process of becoming a major source of computational power for numerical applications. Originally designed for application of time-consuming graphics operations, GPUs are stream processors that implement the SIMD paradigm. The true degree of parallelism of GPUs is often hidden from the user, making programming even more flexible and convenient. In this chapter we survey Genetic Programming methods currently ported to GPUs.

#### SOURCE: Organic Computing R.P. Wuertz (Ed.), Springer, Berlin, Complex Systems Series, 2008, 201 - 220

ABSTRACT: There is growing interest in the use of analogies of biological development for problem solving in computer science. Nature is extremely intricate when compared to human designs, and incorporates features such as the ability to scale, adapt and self-repair that could be usefully incorporated into human-designed artifacts. In this chapter, we discuss how the metaphor of biological development can be used in artificial systems and highlight some of the challenges of this emerging field.

#### SOURCE: Genetic Programming - Theory and Applications U.M. O'Reilly, T. Yu, R. Riolo, and B. Worzel (Eds.), Kluwer Academic, Norwell, MA, 2004, 175 - 190

ABSTRACT: We introduce a new method of execution for GP-evolved programs consisting of register machine instructions. It is shown that this method can be considered as an artificial chemistry. It lends itself well to distributed and parallel computing schemes in which synchronization and coordination are not an issue.

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#### SOURCE: Embodied Artificial Intelligence, F. Iida, R. Pfeifer, L. Steels and Y. Kuniyoshi (Eds.), Springer, Berlin, LNAI 3139, 2004, 284 - 292

ABSTRACT: In this contribution we consider the idea that successful evolutionary design is best achieved in a networked system. We exemplify this thought by a discussion of artificial regulatory networks, a recently devised method to model natural genome-protein interactions. It is argued that emergent phenomena in nature require the existence of networks in order to become permanent.

#### FILENAME: embodied.pdf (565 kB)




#### SOURCE: Frontiers in Evolutionary ComputationA. Menon (Ed.), Kluwer Academic, Boston, MA, 2004, 243 - 260

ABSTRACT: In this chapter we discuss the challenge provided by the problem of evolving large amounts of computer code via Genetic Programming. We argue that the problem is analogous to what Nature had to face when moving to multi-cellular life. We propose to look at developmental processes and there mechanisms to come up with solutions for this ''challenge of complexity'' in Genetic Programming.

#### SOURCE: Genetic Programming - Theory and Applications R. Riolo, B. Worzel (Eds.), Kluwer Academic, Boston, MA, 2003, 43 - 61

ABSTRACT: An artificial regulatory network able to reproduce a number of phenomena found in natural genetic regulatory networks (such as heterochrony, evolution, stability and variety of network behavior) is proposed. The connection to a new genetic representation for Genetic Programming is outlined.

#### SOURCE: On Growth, Form and Computers, P. Bentley und S. Kumar (Eds.), Academic Press, New York, 2003, 278 - 302

FROM THE INTRODUCION ...:
The development of an entire organism from a single cell is one of the most profound and awe inspiring phenomena in the whole of the natural world. The complexity of living systems itself dwarfs anything that man has produced. This is all the more the case for the processes that lead to these intricate systems. In each phase of the development of a multi-cellular being, this living system has to survive, whether stand-alone or supported by various structures and processes provided by other living systems. Organisms construct themselves, out of humble single-celled beginnings, riding waves of interaction between the information residing in their genomes - inherited from the evolutionary past of their species via their progenitors - and the resources of their environment.

#### FILENAME: chapter_finalrevision.pdf (n kB)




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#### EDITORS: W. Banzhaf and F. Eeckman

ABSTRACT: We discuss algorithms based on the RNA interaction found in Nature. Molecular biology has reveiled that strands of RNA, besides being autocatalytic, can interact with each other. They play a double role of being information carriers and enzymes. The first role is realized by the 1-dimensional sequence of nucleotides on a strand of RNA, the second by the 3-dimensional form strands can assume under appropriate temperature and solvent conditions. We use this basic idea of having two alternative forms of the same sequence to propose a new Artificial Life algorithm. After a general introduction to the area we report our findings in a specific application studied recently: an algorithm which allows sequences of binary numbers to interact. We introduce folding methods to achieve 2-dimensional alternative forms of the sequences. Interactions between 1- and 2-dimensional forms of binary sequences generate new sequences, which compete with the original ones due to selection pressure. Starting from random sequences, replicating and self-replicating sequences are generated in considerable numbers. We follow the evolution of a number of sample simulations and analyse the resulting self-organising system.

#### EDITORS: L. Spector, W. Langdon, U. O'Reilly and P. Angeline

ABSTRACT: This chapter describes recent advances in genetic programming of machine code. Evolutionary program induction using binary machine code is the fastest known Genetic Programming method. It is, in addition, the most well studied Genetic Programming system that uses a linear genome. Evolutionary program induction using binary machine code was originally referred to as {\em Compiling Genetic Programming System} (CGPS). For clarity, the name was changed in early 1998 to {\em Automatic Induction of Machine Code---Genetic Programming} (AIM-GP). AIM-GP stores evolved programs as linear strings of native binary machine code, which are directly executed by the processor. The absence of an interpreter and complex memory handling increases the speed of AIM-GP by about two orders of magnitude. AIM-GP has so far been applied to processors with a fixed instruction length (RISC) using integer and floating-point arithmetic. We also describe several recent advances in the AIM-GP technology. Such advances include enabling the induction of code for CISC processors such as the INTEL x86 as well as JAVA and many embedded processors. The new techniques also make AIM-GP more portable in general and simplify the adaptation to any processor architecture. Other additions include the use of floating point instructions, control flow instructions, ADFs and new genetic operators e.g. aligned homologous crossover. This chapter also discusses the benefits and drawbacks of register machine GP versus tree-based GP. This chapter is directed towards the practitioner, who wants to extend AIM-GP to new architectures and application domains.

#### EDITORS: L. Spector, W. Langdon, U. O'Reilly and P. Angeline

ABSTRACT: Surface reconstruction is a hard key problem in the industrial core domain of computer-aided design (CAD) applications. A workpiece must be represented in some standard CAD object description format such that its representation can be efficiently used in a CAD process like redesign. To that end, a digitizing process represents the object surface as a weakly-structured discrete and digitized set of 3D points. Surface reconstruction attempts to transform this representation into an efficient CAD representation. Certain classic approaches produce inefficient reconstructions of surface areas that do not correspond to construction logic. Here, a new reconstruction principle along with empiric results is presented which yields logical and efficient representations. This principle is implemented as a Genetic-Programming/Evolution-Strategy-based software system.

#### EDITORS: W.M.L. Holcombe, R. Paton (Eds.)

ABSTRACT: The signal processing system of a cell is very robust in its dependence upon the observation of central metabolites and, on the other hand, on the hierarchical division into functional blocks. Therefore it can act as a model for the construction of robust, highly parallel and distributed control systems. We have used the metabolic paradigm as a guideline to develop a robot architecture for simple navigation tasks. Results on obstacle avoidance and light searching behavior are reported here.

#### SOURCE: Advances in Genetic Programming II, P. Angeline, K. Kinnear (eds.), MIT Press, Cambridge, MA, 1996, pp. 111 --- 134

ABSTRACT: In Genetic Programming, introns play at least two substantial roles: (1) A structural protection role, allowing the population to preserve highly-fit building blocks; and (2) A global protection role, enabling an individual to protect itself almost entirely against the destructive effect of crossover. We introduce Explicitly Defined Introns into Genetic Programming. Our results suggest that the introduction of Explicitly Defined Introns can improve fitness, generalization, and CPU time. Further, Explicitly Defined Introns partially replace the role of Implicit Introns ( that is, introns that emerge from crossover and mutation without being explicitly defined as such). Finally, Explicitly Defined Introns and Implicit Introns appear, in some situations, to work in tandem to produce better training, fitness and generalization than occurs without Explicitly Defined Introns.

#### FILENAME: aigp2.pdf (214 kB)

Wolfgang Banzhaf
Last updated: Jul 14, 2020