The ARGOT strategy: adaptive representation genetic optimizer technique
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Dynamic Parameter Encoding for Genetic Algorithms
Machine Learning
Engineering optimizations using the structured genetic algorithm
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Artificial Life
Self-replicating structures: evolution, emergence, and computation
Artificial Life - Special issue on self-replication
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Darwinian Genetic Code: An Adaptation for Adapting?
Genetic Programming and Evolvable Machines
Toward Machine Learning Through Genetic Code-like Transformations
Genetic Programming and Evolvable Machines
Computer
Contextual Genetic Algorithms: Evolving Developmental Rules
Proceedings of the Third European Conference on Advances in Artificial Life
Distributed Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Parallel Genetic Algorithms Population Genetics and Combinatorial Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
The Symbiotic Evolution of Solutions and Their Representations
Proceedings of the 6th International Conference on Genetic Algorithms
Parameter-Free Genetic Algorithm Inspired by ``Disparity Theory of Evolution''
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Co-evolutionary Constraint Satisfaction
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
The Density of States - A Measure of the Difficulty of Optimisation Problems
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Genome Growth and the Evolution of the Genotype-Phenotype Map
Evolution and Biocomputation, Computational Models of Evolution
Chemical genetic algorithms: coevolution between codes and code translation
ICAL 2003 Proceedings of the eighth international conference on Artificial life
Theory of Self-Reproducing Automata
Theory of Self-Reproducing Automata
A coevolutionary approach to adapt the genotype-phenotype map in genetic algorithms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Automatic discovery of self-replicating structures in cellularautomata
IEEE Transactions on Evolutionary Computation
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
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A chemical genetic algorithm (CGA) in which several types of molecules (information units) react with each other in a cell is proposed. Not only the information in DNA, but also smaller molecules responsible for the transcription and translation of DNA into amino acids, are adaptively changed during evolution, which optimizes the fundamental mapping from binary substrings in DNA (genotype) to real values for a parameter set (phenotype). Through the struggle between cells containing a DNA unit and small molecular units, the codes (DNA) and the interpreter (the small molecular units) coevolve, and a specific output function, from which a cell's fitness is evaluated, is optimized. To demonstrate the effectiveness of the CGA, it is applied to a set of variable-separable and variable-inseparable problems, and it is shown that the CGA can robustly solve a wide range of optimization problems regardless of their fitness characteristics. To ascertain the optimization of the genotype-to-phenotype mapping by the CGA, we also conduct analytical experiments for some problems while observing the basin size of a global optimum solution in the binary genotype space. The results show that the CGA effectively augments the basin size, makes it easier for evolution to find a path to the global optimum solution, and enhances the GA's evolvability during evolution.