The Creativity Potential Within Evolutionary Algorithms Export

@incollection{citeulike:1624240, abstract = {The traditional GA theory is pillared on the Building Block Hypothesis (BBH) which states that Genetic Algorithms (GAs) work by discovering, emphasizing and recombining low order schemata in high-quality strings, in a strongly parallel manner. Historically, attempts to capture the topological fitness landscape features which exemplify this intuitively straight-forward process, have been mostly unsuccessful. Population-based recombinative methods had been repeatedly outperformed on the special designed abstract test suites, by different variants of mutation-based algorithms. Departing from the BBH, in this paper we seek to exemplify the utility of crossover from a different point of view, emphasizing the creative potential of the crossover operator. We design a special class of abstract test suites, called Trident functions, which exploits the ability of modern GAs to mix good but significantly different solutions. This approach has been so far neglected as it is widely believed that disruption caused by mating individuals that are too dissimilar may be harmful. We anticipate that hybridizing different designs induces a complex neighborhood structure unattainable by trajectory-based methods which can conceal novel solutions. Empirical results confirm that the proposed class of problems can be solved efficiently only by population-based panmictic recombinative methods, employing diversity maintaining mechanisms.}, author = {Icl\u{a}nzan, David}, citeulike-article-id = {1624240}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-540-74913-4_85}, doi = {10.1007/978-3-540-74913-4_85}, journal = {Advances in Artificial Life}, keywords = {benchmark, building-block, comparison, crossover, evolutionary-computation, fitness-landscape, genetic-algorithm, hybridization, metaheuristic, mutation}, pages = {845--854}, posted-at = {2007-09-05 15:14:57}, priority = {3}, title = {The Creativity Potential Within Evolutionary Algorithms}, url = {http://dx.doi.org/10.1007/978-3-540-74913-4_85}, year = {2007} }

TY - CHAP ID - citeulike:1624240 L3 - citeulike-article-id:1624240 N2 - The traditional GA theory is pillared on the Building Block Hypothesis (BBH) which states that Genetic Algorithms (GAs) work by discovering, emphasizing and recombining low order schemata in high-quality strings, in a strongly parallel manner. Historically, attempts to capture the topological fitness landscape features which exemplify this intuitively straight-forward process, have been mostly unsuccessful. Population-based recombinative methods had been repeatedly outperformed on the special designed abstract test suites, by different variants of mutation-based algorithms. Departing from the BBH, in this paper we seek to exemplify the utility of crossover from a different point of view, emphasizing the creative potential of the crossover operator. We design a special class of abstract test suites, called Trident functions, which exploits the ability of modern GAs to mix good but significantly different solutions. This approach has been so far neglected as it is widely believed that disruption caused by mating individuals that are too dissimilar may be harmful. We anticipate that hybridizing different designs induces a complex neighborhood structure unattainable by trajectory-based methods which can conceal novel solutions. Empirical results confirm that the proposed class of problems can be solved efficiently only by population-based panmictic recombinative methods, employing diversity maintaining mechanisms. JF - Advances in Artificial Life EP - 854 TI - The Creativity Potential Within Evolutionary Algorithms SP - 845 KW - benchmark KW - building-block KW - comparison KW - crossover KW - evolutionary-computation KW - fitness-landscape KW - genetic-algorithm KW - hybridization KW - metaheuristic KW - mutation AU - Iclănzan, David PY - 2007/// UR - http://dx.doi.org/10.1007/978-3-540-74913-4_85 ER -