Processor allocation in an N-cube multiprocessor using gray codes
IEEE Transactions on Computers
Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
An adaptive crossover distribution mechanism for genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
The ARGOT strategy: adaptive representation genetic optimizer technique
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Dynamic Parameter Encoding for Genetic Algorithms
Machine Learning
Efficient generation of the binary reflected gray code and its applications
Communications of the ACM
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Using Genetic Algorithms to Solve NP-Complete Problems
Proceedings of the 3rd International Conference on Genetic Algorithms
Sizing Populations for Serial and Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Serial and Parallel Genetic Algorithms as Function Optimizers
Proceedings of the 5th International Conference on Genetic Algorithms
Optimization Using Distributed Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Genetic Algorithms and Evolution Strategies - Similarities and Differences
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Coding and Information Theory
Efficiency speed-up strategies for evolutionary computation: fundamentals and fast-GAs
Applied Mathematics and Computation
Evolutionary Computation
Forking genetic algorithms: Gas with search space division schemes
Evolutionary Computation
Implicit representation in genetic algorithms using redundancy
Evolutionary Computation
Search space division in GAs using phenotypic properties
Information Sciences: an International Journal
Representation, search and genetic algorithms
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Evolutionary computation and its applications in neural and fuzzy systems
Applied Computational Intelligence and Soft Computing
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Delta coding is an iterative genetic search strategy that dynamically changes the representation of the search space in an attempt to exploit different problem representations. Delta coding sustains search by reinitializing the population at each iteration of search. This helps to avoid the asymptotic performance typically observed in genetic search as the population becomes more homogeneous. Here, the optimization ability of delta coding is empirically compared against CHC, ESGA, GENITOR, and random mutation hill-climbing (RMHC) on a suite of well-known test functions with and without Gray coding. Issues concerning the effects of Gray coding on these test functions are addressed.