Parallel genetic algorithms, population genetics and combinatorial optimization
Proceedings of the third international conference on Genetic algorithms
Distributed genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Environmental effects on the coevolution of pursuit and evasion strategies
Genetic Programming and Evolvable Machines
Evolutionary design of a DDPD model of ligation
EA'05 Proceedings of the 7th international conference on Artificial Evolution
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
| Hi-index | 0.00 |
A chemical genetic algorithm (CGA) in which several types of molecules (information units) react with each other in a cell is proposed. Translation from codons (short substrings of bits) in DNA to amino acids (real value units) is specified by a particular set of translation molecules created by the reaction between tRNA units and amino acid units. This adaptively changes and optimizes the fundamental genotype-phenotype mapping during evolution. Through the struggle between cells containing a DNA unit and small molecular units, the codes in DNA and the translation table described by the small molecular units coevolve, and a specific output function (protein), which is used to evaluate a cell's fitness, is optimized. To demonstrate the effectiveness of the CGA, the algorithm is applied to a set of deceptive problems, and the results by using the CGA are compared to those by using a simple GA. It is shown that the CGA has far better performance for the tested functions than the conventional simple GA.