The notion of ``evolvability''---the ability of a population to produce variants fitter than any yet existing---is developed as it applies to genetic algortithms. A theoretical analysis of the dynamics of genetic programming predicts the existence of a novel, emergent selection phenomenon: the evolution of evolvability. This is produced by the proliferation, within programs, of blocks of code that have a higher chance of increasing fitness when added to programs. Selection can then come to mold the variational aspects of the way evolved programs are represented. A model of code proliferation within programs is analyzed to illustrate this effect. The mathematical and conceptual framework includes: the definition of evolvability as a measure of performance for genetic algorithms; application of Price's Covariance and Selection Theorem to show how the fitness function, representation, and genetic operators must interact to produce evolvability---namely, that genetic operators produce offspring with fitnesses specifically correlated with their parent's fitnesses; how blocks of code emerge as a new level of replicator, proliferating as a function of their ``constructional fitness,'' which is distinct from their schema fitness; and how programs may change from innovative code to conservative code as the populations mature. Several new selection techniques and genetic operators are proposed in order to give better control over the evolution of evolvability and improved evolutionary performance.

The evolution of evolvability in genetic programming

This thesis investigates the evolution and use of abstract data types within Genetic Programming (GP). In genetic programming the principles of natural evolution (fitness based selection and recombination) acts on program code to automatically generate computer programs. The research in this thesis is motivated by the observation from software engineering that data abstraction (e.g. via abstract data types) is essential in programs created by human programmers. We investigate whether abstract data types can be similarly beneficial to the automatic production of programs using GP. GP can automatically "evolve" programs which solve non-trivial problems but few experiments have been reported where the evolved programs explicitly manipulate memory and yet memory is an essential component of most computer programs. So far work on evolving programs that explicitly use memory has principally used either problem specific memory models or a simple indexed memory model consisting of a single global shared array. Whilst the latter is potentially sufficient to allow any computation to evolve, it is unstructured and allows complex interaction between parts of programs which weaken their modularity. In software engineering this is addressed by controlled use of memory using scoping rules and abstract data types, such as stacks, queues and files. This thesis makes five main contributions: (1) Proving that abstract data types (stacks, queues and lists) can be evolved using genetic programming. (2) Demonstrating GP can evolve general programs which recognise a Dyck context free language, evaluate Reverse Polish expressions and GP with an appropriate memory structure can solve the nested brackets problem which had previously been solved using a hybrid GP. (3) In these three cases (Dyck, expression evaluation and nested brackets) an appropriate data structure is proved to be beneficial compared to indexed memory. (4) Investigations of real world electrical network maintenance scheduling problems demonstrate that Genetic Algorithms can find low cost viable solutions to such problems. (5) A taxonomy of GP is presented, including a critical review of experiments with evolving memory. These contributions support our thesis that data abstraction can be beneficial to automatic program generation via artificial evolution.

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Genetic programming and data structures

Acknowledgments xxi Acronyms xxiii List of Algorithms xxvii Pert I: Introduction to Evolutionary Optimization 1 Introduction 1 2 Optimization 11 Part II: Classic Evoluntionary Algorithms 3 Generic Algorithms 35 4 Mathematical Models of Genetic Algorithms 63 5 Evolutionary Programming 95 6 Evolution Strategies 117 7 Genetic Programming 141 8 Evolutionary Algorithms Variations 179 Part III: More Recent Evolutionary Algorithms 9 Simulated Annealing 223 10 Ant Colony Optimization 241 11 Particle Swarm Optimization 265 12 Differential Evolution 293 13 Estimation of Distribution Algorithms 313 14 Biogeography-Based Optimization 351 15 Cultural Algorithms 377 16 Oppostion-Based Learning 397 17 Other Evolutionary Algorithms 421 Part IV: Special Type of Optimization Problems 18 Combinatorial Optimization 449 19 Constrained Optimization 481 20 Multi-Objective Optimization 517 21 Expensive, Noisy and Dynamic Fitness Functions 563 Appendices A Some Practical Advice 607 B The No Free Luch Therorem and Performance Testing 613 C Benchmark Optimization Functions 641

Evolutionary optimization algorithms : biologically-Inspired and population-based approaches to computer intelligence

Tag is a children’s game based on symmetrical pursuit and evasion. In the experiments described here, control programs for mobile agents (simulated vehicles) are evolved based on their skill at the game of tag. A player’s fitness is determined by how well it performs when placed in competition with several opponents chosen randomly from the coevolving population of players. In the beginning, the quality of play is very poor. Then slightly better strategies begin to exploit the weaknesses of others. Through evolution, guided by competitive fitness, increasingly better strategies emerge over time.

Competition, Coevolution and the Game of Tag

A key concern in genetic programming GP is the size of the state space which must be searched for large and complex problem do mains One method to reduce the state space size is by using Strongly Typed Genetic Programming STGP We applied both GP and STGP to construct cooperation strate gies to be used by multiple predator agents to pursue and capture a prey agent on a grid world This domain has been extensively studied in Distributed Arti cial Intelligence DAI as an easy to describe but di cult to solve cooperation problem The evolved programs from our systems are competitive with manually derived greedy algorithms In particular the STGP paradigm evolved strategies in which the predators were able to achieve their goal without explicitly sens ing the location of other predators or com municating with other predators This is an improvement over previous research in this area The results of our experiments indicate that STGP is able to evolve programs that perform signi cantly better than GP evolved programs In addition the programs gener ated by STGP were easier to understand

Strongly Typed Genetic Programming in Evolving Cooperation Strategies

Alternatives in automatic function definition: a comparison of performance

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Alternatives in automatic function definition: a comparison of performance

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The evolution of evolvability in genetic programming

Genetic programming and data structures

Evolutionary optimization algorithms : biologically-Inspired and population-based approaches to computer intelligence

Competition, Coevolution and the Game of Tag

Strongly Typed Genetic Programming in Evolving Cooperation Strategies

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