Dynamic Parameter Encoding for Genetic Algorithms
Machine Learning
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A New Diploid Scheme and Dominance Change Mechanism for Non-Stationary Function Optimization
Proceedings of the 6th International Conference on Genetic Algorithms
Enhancing Evolutionary Computation Using Analogues of Biological Mechanisms
Selected Papers from AISB Workshop on Evolutionary Computing
Changing representations during search: A comparative study of delta coding
Evolutionary Computation
Empirical studies of the genetic algorithm with noncoding segments
Evolutionary Computation
Ptgas---genetic algorithms evolving noncoding segments by means of promoter/terminator sequences
Evolutionary Computation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Evolving structural design solutions using an implicit redundant Genetic Algorithm
Structural and Multidisciplinary Optimization
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A new representation combining redundancy and implicit fitness constraints is introduced that performs better than a simple genetic algorithm (GA) and a structured GA in experiments. The implicit redundant representation (IRR) consists of a string that is over-specified, allowing for sections of the string to remain inactive during function evaluation. The representation does not require the user to prespecify the number of parameters to evaluate or the location of these parameters within the string. This information is obtained implicitly by the fitness function during the GA operations. The good performance of the IRR can be attributed to several factors: less disruption of existing fit members due to the increased probability of crossovers and mutation affecting only redundant material; discovery of fit members through the conversion of redundant material into essential information; and the ability to enlarge or reduce the search space dynamically by varying the number of variables evaluated by the fitness function. The IRR GA provides a more biologically parallel representation that maintains a diverse population throughout the evolution process. In addition, the IRR provides the necessary flexibility to represent unstructured problem domains that do not have the explicit constraints required by fixed representations.