An introduction to genetic algorithms
An introduction to genetic algorithms
Adaptive individuals in evolving populations: models and algorithms
Adaptive individuals in evolving populations: models and algorithms
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithm and Graph Partitioning
IEEE Transactions on Computers
Random MAX SAT, random MAX CUT, and their phase transitions
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparison between cellular encoding and direct encoding for genetic neural networks
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
A hybrid system for planning the development level of resort
Expert Systems with Applications: An International Journal
A decision support model for scheduling exhibition projects in art museums
Expert Systems with Applications: An International Journal
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The process of development creates a phenotype from one or more genotypes of an individual through interaction with an environment. The opportunity for development to choose a phenotype from a set of alternatives made possible by the individual's genotype(s) has not been widely considered in evolutionary computation. We briefly review recent research on developmental learning, dominance, and hybrid genetic algorithms that has investigated the role of choice in development. A new model of probabilistic development is presented based upon genotypes that encode the probabilities that the various alleles are expressed in the phenotype. The model outperforms a standard, binary haploid model on two families of single-peaked fitness functions in terms of average fitness. The standard model performed better on multi-peaked MAXSAT environments. More research is needed to fully evaluate the new model.