An analysis of reproduction and crossover in a binary-coded genetic algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
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Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic programming: on the programming of computers by means of natural selection
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Non-coding DNA and floating building blocks for the genetic algorithm
Non-coding DNA and floating building blocks for the genetic algorithm
Explicitly defined introns and destructive crossover in genetic programming
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SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
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A comparison of the fixed and floating building block representation in the genetic algorithm
Evolutionary Computation
Implicit representation in genetic algorithms using redundancy
Evolutionary Computation
On the Feasibility Problem of Penalty-Based Evolutionary Algorithms for Knapsack Problems
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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Pattern Recognition Letters
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In this article we present work on chromosome structures for genetic algorithms (GAs) based on biological principles. Mainly, the influence of noncoding segments on GA behavior and performance is investigated. We compare representations with noncoding sequences at predefined, fixed locations with “junk” code induced by the use of promoter/terminator sequences (ptGAs) that define start and end of a coding sequence, respectively. AS one of the advantages of noncoding segments a few researchers have identified the reduction of the disruptive effects of crossover, and we solidify this argument by a formal analysis of crossover disruption probabilities for noncoding segments at fixed locations. The additional use of promoter/terminator sequences not only enables evolution of parameter values, but also allows for adaptation of number, size, and location of genes (problem parameters) on an artificial chromosome. Randomly generated chromosomes of fixed length carry different numbers of promoter/terminator sequences resulting in genes of varying size and location. Evolution of these ptGA chromosomes drives the number of parameters and their values to (sub)optimal solutions. Moreover, the formation of tightly linked building blocks is enhanced by self-organization of gene locations. We also introduce a new, nondisruptive crossover operator emerging from the ptGA gene structure with adaptive crossover rate, location, and number of crossover sites. For experimental comparisons of this genetic operator to conventional crossover in GAs, as well as properties of different ptGA chromosome structures, an artificial problem from the literature is utilized. Finally, the potential of ptGA is demonstrated on an NP-complete combinatorial optimization problem.