Power programming with RPC
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Parallel recombinative simulated annealing: a genetic algorithm
Parallel Computing
Penalty guided genetic search for reliability design optimization
Computers and Industrial Engineering
An efficient evolutionary programming algorithm
Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Some Guidelines for Genetic Algorithms with Penalty Functions
Proceedings of the 3rd International Conference on Genetic Algorithms
Image segmentation using evolutionary computation
IEEE Transactions on Evolutionary Computation
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Today's industrial environment requires engineering design to be achieved by geographically distributed engineering teams who may work on different computer platforms, so the analogy can be presented as the distributed constraint optimization problems. This paper presents an agile approach that carries out a concurrent optimization of a product design and its associate constraint satisfaction in manufacturing perspective. Also, the approach has been implemented through the World Wide Web (WWW) regardless of the geographical constraints and different platforms used. In this paper, the hybrid evolution computation (EC) approaches combing genetic algorithm and stochastic annealing algorithms are applied to find optimal or near optimal solutions for two engineering design cases. The main contribution of this paper is to provide an agile approach for solving the engineering design problem which is modeled by the nonlinear programming model, and the approach is implemented through the WWW regardless of the geographical constraints and different platforms used. Experimental results are presented to exhibit the superior performance of the proposed methodology.