ABSTRACT
Strategies for solving different types of problems can be represented as a finite state machine (FSM). For such problems, finite state machines are being used as the genotype (operand) for genetic algorithms (GAs) in artificial life and artificial intelligence research. Algorithms which are FSM-specific and which were designed to improve the convergence of the genetic algorithm for FSM genomes are presented. Because a single finite state machine has different representations (simply by changing state names), two reorganization operators (named SFS and MTF) were developed so identical machines would appear the same and not have to compete against each other for their share of the next generation. The algorithms were designed with the intent of enhancing schemata growth for an FSM genome by reorganizing a population of these machines during run time. Experiments were performed with these new operators in order to determine how they would affect the convergence of genetic algorithms.
