Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Dynamic Parameter Encoding for Genetic Algorithms
Machine Learning
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Genetic Algorithms, Operators, and DNA Fragment Assembly
Machine Learning - Special issue on applications in molecular biology
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A cell exclusion algorithm for determining all the solutions of a nonlinear system of equations
Applied Mathematics and Computation
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Applied Mathematics and Computation
Towards designing artificial neural networks by evolution
Applied Mathematics and Computation - Special issue on articficial life and robotics
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Raising Theoretical Questions About the Utility of Genetic Algorithms
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Changing representations during search: A comparative study of delta coding
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Empirical investigation of the benefits of partial lamarckianism
Evolutionary Computation
Forking genetic algorithms: Gas with search space division schemes
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
An evolutionary strategy for global minimization and its Markovchain analysis
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Macroevolutionary algorithms: a new optimization method on fitnesslandscapes
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A new model of simulated evolutionary computation-convergenceanalysis and specifications
IEEE Transactions on Evolutionary Computation
Hybrid methods using genetic algorithms for global optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A splicing/decomposable encoding and its novel operators for genetic algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Neural Network Training Using Genetic Algorithm with a Novel Binary Encoding
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
A mixed strategy of combining evolutionary algorithms with multigrid methods
International Journal of Computer Mathematics
A New Approach to Pattern Recognition in Fractal Ferns
International Journal of Artificial Life Research
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Through identifying the main causes of low efficiency of the currently known evolutionary algorithms, a set of six efficiency speed-up strategies are suggested, analyzed, and partially explored, including those of the splicing/decomposable representation scheme, the exclusion-based selection operators, the "few-generation-ended" EC search, the "low-resolution" computation with reinitialization, and the coevolution-like decomposition. Incorporation of the strategies with any known evolutionary algorithm leads to an accelerated version of the algorithm. On the basis of problem space discretization, the proposed strategies accelerate evolutionary computation via a "best-sofar solution" guided, exclusion-based space-shrinking scheme. With this scheme, an arbitrarily high-precision (resolution) solution of a high-dimensional problem can be obtained by means of a successive low-resolution search in low-dimensional search spaces. As a case study, the developed strategies have been endowed with genetic algorithms (GAs), yielding an accelerated genetic evolutionary algorithm: the fast GAs. The fast-GAs are experimentally tested with a test suit containing 10 complex multimodal function optimization problems and a difficult real-life problem--the moment matching problem for inverse to the fractal encoding of the spleenwort fern. The performance of the fast-GA is compared against the standard GA (SGA) and the forking GA (FGA) (that is one of the most recent and fairly established variants of GAs). The experiments all demonstrate that the fast-GAs consistently and significantly outperform the SGA and FGA in efficiency and solution quality in the test cases. Besides the speed-up of efficiency, other visible features of the fast-GAs include: (i) no premature convergence occurs; (ii) better convergence capability to global optimum; and (iii) variable high-precision solutions attainable. All these support the validity and usefulness of the developed strategies.