5 research outputs found

    Adaptive Peircean decision aid project summary assessments.

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    Behavior Of Variable-length Genetic Algorithms Under Random Selection

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    In this work, we show how a variable-length genetic algorithm naturally evolves populations whose mean chromosome length grows shorter over time. A reduction in chromosome length occurs when selection is absent from the GA. Specifically, we divide the mating space into five distinct areas and provide a probabilistic and empirical analysis of the ability of matings in each area to produce children whose size is shorter than the parent generation\u27s average size. Diversity of size within a GA\u27s population is shown to be a necessary condition for a reduction in mean chromosome length to take place. We show how a finite variable-length GA under random selection pressure uses 1) diversity of size within the population, 2) over-production of shorter than average individuals, and 3) the imperfect nature of random sampling during selection to naturally reduce the average size of individuals within a population from one generation to the next. In addition to our findings, this work provides GA researchers and practitioners with 1) a number of mathematical tools for analyzing possible size reductions for various matings and 2) new ideas to explore in the area of bloat control

    Representation development from Pareto-Coevolution

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    Abstract. Genetic algorithms generally use a fixed problem representation that maps variables of the search space to variables of the problem, and operators of variation that are fixed over time. This limits their scalability on non-separable problems. To address this issue, methods have been proposed that coevolve explicitly represented modules. An open question is how modules in such coevolutionary setups should be evaluated. Recently, Pareto-coevolution has provided a theoretical basis for evaluation in coevolution. We define a notion of functional modularity, and objectives for module evaluation based on Pareto-Coevolution. It is shown that optimization of these objectives maximizes functional modularity. The resulting evaluation method is developed into an algorithm for variable length, open ended development of representations called DevRep. DevRep successfully identifies large partial solutions and greatly outperforms fixed length and variable length genetic algorithms on several test problems, including the 1024-bit Hierarchical-XOR problem
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