In this paper, I describe the application of genetic programming to evolve a controller for a robotic tank in a simulated environment. The purpose is to explore how genetic techniques can best be applied to produce controllers based on subsumption and behavior oriented languages such as REX. As part of my implementation, I developed TableRex, a modification of REX that can be expressed on a fixed-length genome. Using a fixed subsumption architecture of TableRex modules, I evolved robots that beat some of the most competitive hand-coded adversaries.

In this paper, I describe the application of genetic programming to evolve a controller for a robotic tank in a simulated environment.The purpose is to explore how genetic techniques can best be applied to produce controllers based on subsumption and behavior oriented languages such as REX. As part of my implementation, I developed TableRex, a modification of REX that can be expressed on a fixed-lengthgenome. Using a fixed subsumption architecture of TableRex modules, I evolved robots that beat some of the most competitive hand-coded adversaries.

This thesis is an investigation into Supervised Learning (SL) in Genetic Programming (GP). With its flexible tree-structured representation, GP is a type of Genetic Algorithm, using the Darwinian idea of natural selection and genetic recombination, evolving populations of solutions over many generations to solve problems. SL is a common approach in Machine Learning where the problem is presented as a set of examples. A good or fit solution is one which can successfully deal with all of the examples. In common with most Machine Learning approaches, GP has been used to solve many trivial problems. When applied to larger and more complex problems, however, several difficulties become apparent. When focusing on the basic features of GP, this thesis highlights the immense size of the GP search space, and describes an approach to measure this space. A stupendously flexible but frustratingly useless representation, Anarchically Automatically Defined Functions, is described. Some difficulties associated with the normal use of the GP operator Crossover (perhaps the most common method of combining GP trees to produce new trees) are demonstrated in the simple MAX problem. Crossover can lead to irreversible sub-optimal GP performance when used in combination with a restriction on tree size. There is a brief study of tournament selection which is a common method of selecting fit individuals from a GP population to act as parents in the construction of the next generation. The main contributions of this thesis however are two approaches for avoiding the fitness evaluation bottleneck resulting from the use of SL in GP. to establish the capability of a GP individual using SL, it must be tested or evaluated against each example in the set of training examples.

No description available.

This research investigates the integration of evolutionary techniques for symbolic regression. In particular the genetic programming paradigm is used together with other evolutionary computational techniques to develop novel approaches to the improvement of areas of simple preliminary design software using empirical data sets. It is shown that within this problem domain, conventional genetic programming suffers from several limitations, which are overcome by the introduction of an improved genetic programming strategy based on node complexity values, and utilising a steady state algorithm with subpopulations. A further extension to the new technique is introduced which incorporates a genetic algorithm to aid the search within continuous problem spaces, increasing the robustness of the new method. The work presented here represents an advance in the Geld of genetic programming for symbolic regression with significant improvements over the conventional genetic programming approach. Such improvement is illustrated by extensive experimentation utilising both simple test functions and real-world design examples.

Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2006. / Includes bibliographical references (p. 47-48).

This thesis presents Genetic Algorithm for Deriving Software (Gads), a new technique for genetic programming. Gads combines a conventional genetic algorithm with a context-sensitive grammar. The key to Gads is the onto genic mapping, which converts a genome from an array of integers to a correctly typed program in the phenotype language defined by the grammar. A new type of grammar, the reflective attribute grammar (rag), is introduced. The rag is an extension of the conventional attribute grammar, which is designed to produce valid sentences, not to recognize or parse them. Together, Gads and rags provide a scalable solution for evolving type-correct software in independently-chosen context-sensitive languages. The statistics of performance comparison is investigated. A method for representing a set of genetic programming systems or problems on a cladogram is presented. A method for comparing genetic programming systems or problems on a single rational scale is proposed.

No description available.

No description available.

This research applies the biologically inspired, artificial evolutionary processes of Genetic Algorithms and Genetic Programming to digital hardware circuit synthesis and minimization. In this new application, three approaches are taken to genetic hardware development. First, as a method for logic synthesis, Genetic Programming is applied to the building of logic functions. Experimental results have shown the logic equations from this technique produce better than 88% coverage of the given truth-tables, but the method cannot guarantee complete (100%) coverage. Secondly, to better achieve complete function coverage, an XOR Correction Circuit Algorithm used in conjunction with the Genetic Logic Synthesis was developed. With this algorithm, the genetic logic synthesis can reiteratively attempt coverage by formulating its own selective "correction" functions, for input combinations where complete truth table coverage has not previously been achieved. With this technique, complete function coverage was synthesized in all experiments conducted. The third application of the paradigm is to the minimization of Reed-Muller Equations. In this application, a Genetic Algorithm is implemented only in the search space of all "correct", functionally equivalent equations, with only the task of finding reductions. With this limited search space the solutions have absolute guaranteed function coverage, as well as a better defined focus for the genetic evolutionary process. In both the logic synthesis and minimization processes the genetic operators determine efficient circuit implementations and reductions. The results are often different from those of human designers. Because the genetic techniques incorporate logical testing into the design and build process, one can be assured that the circuit will function as derived on completion. For all three applications, the effects of a number of evolutionary parameters on the genetic operators' problem solving capability are examined. The resulting logic and logic minimizations are also compared with both arbitrarily defined functions and well known logic synthesis benchmarks. It has been shown that genetic operators applied to digital logic can effectively find good solutions for both logic synthesis and logic minimization. / Graduation date: 1997