Abstract. In this work, we investigate the ability of a Chunking GA (ChGA) to reduce the size of variable length chromosomes and control bloat. The ChGA consists of a standard genetic algorithm augmented by a communal building block memory system and associated memory chromosomes and operators. A new mxn MaxSum fitness function used for this work is also described. Results show that a ChGA equipped with memory capacity equal to or greater than the minimal size of an optimal solution naturally eliminates unexpressed genes.
In this work we provide empirical evidence that shows how a variable-length genetic algorithm (GA) can naturally evolve shorter average size populations. This reduction in chromosome length appears to occur in finite population GAs when 1) selection is absent from the GA (random) or 2) when selection focuses on some other property not influenced by the length of individuals within a population.
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