Local search operators in fast evolutionary programming

Birru, H.K.; Chellapilla, Kumar; Rao, S.S.

doi:10.1109/cec.1999.782662

Cited by 16 publications

(7 citation statements)

References 11 publications

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“…Cauchy distribution, on the other hand, is a more slowly decaying distribution in comparison with Gaussian and allows even the large mutation step sizes. Cauchy distribution has reportedly outperformed Gaussian distribution in several instances available in literature (Lan and Lan 2008;Rudolph 1997;Yao et al 1999;Birru et al 1999;Wang et al 2006;Coelho and Krohling 2003).…”

Section: Application Of Cauchy Mutationmentioning

confidence: 97%

Improving the performance of differential evolution algorithm using Cauchy mutation

Ali

Pant

2010

Soft Comput

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Differential evolution (DE) is a powerful yet simple evolutionary algorithm for optimization of realvalued, multimodal functions. DE is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from premature convergence and/or slow convergence rate resulting in poor solution quality and/or larger number of function evaluation resulting in large CPU time for optimizing the computationally expensive objective functions. Therefore, an attempt to speed up DE is considered necessary. This research introduces a modified differential evolution (MDE) that enhances the convergence rate without compromising with the solution quality. The proposed MDE algorithm maintains a failure_counter (FC) to keep a tab on the performance of the algorithm by scanning or monitoring the individuals. Finally, the individuals that fail to show any improvement in the function value for a successive number of generations are subject to Cauchy mutation with the hope of pulling them out of a local attractor which may be the cause of their deteriorating performance. The performance of proposed MDE is investigated on a comprehensive set of 15 standard benchmark problems with varying degrees of complexities and 7 nontraditional problems suggested in the special session of CEC2008. Numerical results and statistical analysis show that the proposed modifications help in locating the global optimal solution in lesser numbers of function evaluation in comparison with basic DE and several other contemporary optimization algorithms.

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Section: Application Of Cauchy Mutationmentioning

confidence: 97%

Improving the performance of differential evolution algorithm using Cauchy mutation

Ali

Pant

2010

Soft Comput

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“…In the standard DE new individuals are generated with the information of different individuals which may leads to good explorative global searching power, but the local searching power near each individual especially the best ones is relatively poor. An early published paper about evolutionary programming (Birru et al 1999) also indicated that local searching helps to generate global optimum if the corresponding algorithm could find the basin of attraction for global optimum and thus reduce the time for the algorithm to converge. Thus the explorative power and exploitive power should be balanced to reach better performance.…”

Section: Introductionmentioning

confidence: 99%

A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training

Yang

Zhang

2015

Cogn Neurodyn

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Extreme learning machine (ELM) is a novel and fast learning method to train single layer feed-forward networks. However due to the demand for larger number of hidden neurons, the prediction speed of ELM is not fast enough. An evolutionary based ELM with differential evolution (DE) has been proposed to reduce the prediction time of original ELM. But it may still get stuck at local optima. In this paper, a novel algorithm hybridizing DE and metaheuristic coral reef optimization (CRO), which is called differential evolution coral reef optimization (DECRO), is proposed to balance the explorative power and exploitive power to reach better performance. The thought and the implement of DECRO algorithm are discussed in this article with detail. DE, CRO and DECRO are applied to ELM training respectively. Experimental results show that DECRO-ELM can reduce the prediction time of original ELM, and obtain better performance for training ELM than both DE and CRO.

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“…A successful MA is an algorithm composed of several well-coordinated components with a proper balance between the global and the local search, which improves the efficiency of searches [2]. Many MAs have been applied to numerical optimization problems, such as a hybrid genetic algorithm with local search (GA-LS) [3], [4], [5], differential evolution with local search (DE-LS) [2], [6], [7], particle swarm optimization with local search (PSO-LS) [8], [9], [10], and evolutionary programming with local search (EP-LS) [11]. Genetic algorithms (GAs) [12] are a very popular class of EAs, they are the most studied population based algorithms.…”

Section: Introductionmentioning

confidence: 99%

Genetic Local Search Algorithm with Self-Adaptive Population Resizing for Solving Global Optimization Problems

Ali

2014

IJIEEB

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Abstract-In the past decades, many types of nature inspired optimization algorithms have been proposed to solve unconstrained global optimization problems. In this paper, a new hybrid algorithm is presented for solving the nonlinear unconstrained global optimization problems by combining the genetic algorithm (GA) and local search algorithm, which increase the capability of the algorithm to perform wide exploration and deep exploitation. The proposed algorithm is called a Genetic Local Search Algorithm with Self-Adaptive Population Resizing (GLSASAPR). GLSASAPR employs a self-adaptive population resizing mechanism in order to change the population size NP during the evolutionary process. Moreover, a new termination criterion has been applied in GLSASAPR, which is called population vector (PV) in order to terminate the search instead of running the algorithm without any enhancement of the objective function values. GLSASAPR has been compared with eight relevant genetic algorithms on fifteen benchmark functions. The numerical results show that the proposed algorithm achieves good performance and it is less expensive and cheaper than the other algorithms.

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Local search operators in fast evolutionary programming

Cited by 16 publications

References 11 publications

Improving the performance of differential evolution algorithm using Cauchy mutation

Improving the performance of differential evolution algorithm using Cauchy mutation

A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training

Genetic Local Search Algorithm with Self-Adaptive Population Resizing for Solving Global Optimization Problems

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