Empirical evaluation of changing crossover operators to solve function optimization problems

@article{Takahashi2016EmpiricalEO,
title={Empirical evaluation of changing crossover operators to solve function optimization problems},
author={R. Takahashi},
journal={2016 IEEE Symposium Series on Computational Intelligence (SSCI)},
year={2016},
pages={1-10}
}
• R. Takahashi
• Published 2016
• Computer Science
• 2016 IEEE Symposium Series on Computational Intelligence (SSCI)
In this paper, the effectiveness of methodologies for changing crossover operators (CXOs) to solve function optimization problems (FOP) are empirically validated in order to solve the problems of premature convergence in genetic algorithms. CXOs are methods of finding solutions for combinatorial optimization problems through genetic algorithms (GAs) while maintaining the balance of satisfying the contrary requisites for GAs: to sustain the diversity of the population and to improve the… Expand
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References

SHOWING 1-10 OF 37 REFERENCES
Solving the Traveling Salesman Problem through Iterative Extended Changing Crossover Operators
• R. Takahashi
• Computer Science
• 2011 10th International Conference on Machine Learning and Applications and Workshops
• 2011
The diversity of generations is strictly measured by the entropy H in Thermo Dynamical Genetic Algorithm (TDGA) and the validity of i-ECXO is experimentally verified by using medium sized TSP data. Expand
Quantitative evaluation of iterative extended changing crossover operators to solve the traveling salesman problem: Diversity measurement and its application to selection strategies in genetic algorithms
• R. Takahashi
• Mathematics, Computer Science
• 2014 10th International Conference on Natural Computation (ICNC)
• 2014
i-ECXO is a hybrid method to unite Edge Assembly Crossover to Ant Colony Optimization, immune Genetic Algorithm realizes Jerne's network which harmonizes the immune system by making another antibodies work antigens for each antibody, and above three methods are evaluated by using the edges' entropy H in TDGA. Expand
The Frontiers of Real-coded Genetic Algorithms
A generation alternation model called JGG (just generation gap) suited for multi-parental crossovers, and a crossover called REX(φ,n + k) as a generlization of the e-UNDX, where φ and n + k denote some probability distribution and the number of parents respectively. Expand
A genetic solution for the traveling salesman problem by means of a thermodynamical selection rule
• Mathematics, Computer Science
• Proceedings of IEEE International Conference on Evolutionary Computation
• 1996
The authors apply a novel selection rule, the Thermodynamical Genetic Algorithm (TDGA), proposed by N. Mori et al. (1995) to the traveling salesman problem (TSP), and propose an adaptive annealing schedule of the temperature in TDGA. Expand
Verification of thermo-dynamical genetic algorithm to solve the function optimization problem through diversity measurement — Diversity measurement and its application to selection strategies in genetic algorithms
• R. Takahashi
• Mathematics, Computer Science
• 2016 IEEE Congress on Evolutionary Computation (CEC)
• 2016
In this paper, it is experimentally verified that TDGA (Thermo Dynamical Genetic Algorithm) is effective in solving a function optimization problem using Genetic Algorithms, because of itsExpand
An improved binary-real coded genetic algorithm for real parameter optimization
• Computer Science
• 2011 Third World Congress on Nature and Biologically Inspired Computing
• 2011
The quality and time performance of BRGA against the benchmark suite and in comparison with original component algorithms (BGA and RGA) is reported and analyzed and is compared with other Evolutionary Algorithms (EAs) from the literature. Expand
A crossover operator using independent component analysis for real-coded genetic algorithms
• Mathematics
• Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546)
• 2001
For real-coded genetic algorithms, there have been proposed many crossover operators. The blend crossover (BLX-/spl alpha/) proposed by L.J. Eshelman and J.D. Schaffer (1993) shows a good searchingExpand
An evolutionary algorithm for large traveling salesman problems
• Mathematics, Medicine
• IEEE Trans. Syst. Man Cybern. Part B
• 2004
Findings imply that the proposed method can find tours robustly with a fixed small population and a limited family competition length in reasonable time, when used to solve large TSPs. Expand
Multiple solution search based on hybridization of real-coded evolutionary algorithm and quasi-newton method
• Mathematics, Computer Science
• 2007 IEEE Congress on Evolutionary Computation
• 2007
Two hybrid algorithms combining real-coded evolutionary computation algorithms and gradient search methods for multiple-solution search in multimodal optimization problems are proposed and a new evaluation function of solution candidates with gradient is presented and discussed in order to find quasi-optimal solutions. Expand
Empirical review of standard benchmark functions using evolutionary global optimization
• Computer Science, Mathematics
• ArXiv
• 2012
We have employed a recent implementation of genetic algorithms to study a range of standard benchmark functions for global optimization. It turns out that some of them are not very useful asExpand