An improved class of real-coded Genetic Algorithms for numerical optimization✰

Abstract Over the last few decades, many improved Evolutionary Algorithms (EAs) have been proposed to tackle different types of optimization problems. Genetic Algorithm (GA) among other canonical algorithms have not shown consistent performance over a range of different optimization problems with complex characteristics. In this paper, an improved class of real-coded Genetic Algorithm is introduced to solve complex optimization problems. The first algorithm, Genetic Algorithm embedded with a new Differential Evolution crossover, GA–DEx, proposes a new variant of Differential Evolution mutation which is used as a new multi-parent crossover in Genetic Algorithms. The main purpose of this algorithm is to enhance the search ability of the GA algorithm by combining a new Differential Evolution crossover with a GA algorithm to avoid premature convergence and stagnation scenarios by exploring more solutions in the problem search space. The second amalgam algorithm, GA–DExSPS, uses an effective and efficient successful parent selection strategy to provide a successful alternative for the selection of parents during the Differential Evolution crossover process. This strategy improves the performance of first introduced algorithm by selecting more promising parents to guide the evolutionary search. The third algorithm, GA–aDExSPS, introduces an aging mechanism and a success-history-based adaptive Genetic Algorithm. This algorithm adapts the alpha parameter used by Differential Evolution crossover in a history-based adaptive manner. This adaptation helps the search to discover more promising regions and to prevent stagnation and premature convergence scenarios. To verify the performance of our class, a challenging test suite of 30 benchmark functions from the IEEE CEC2014 real parameter single objective competition is used. The results affirm the effectiveness and robustness of the proposed algorithms compared to other state-of-the-art well-known crossovers and recent Genetic Algorithms variants.

[1]  Ruhul A. Sarker,et al.  Analyzing the Simple Ranking and Selection Process for Constrained Evolutionary Optimization , 2008, Journal of Computer Science and Technology.

[2]  F. Wilcoxon Individual Comparisons by Ranking Methods , 1945 .

[3]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[4]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[5]  H. Kita,et al.  A crossover operator using independent component analysis for real-coded genetic algorithms , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[6]  Yuping Wang,et al.  A new hybrid genetic algorithm for job shop scheduling problem , 2012, Comput. Oper. Res..

[7]  Anne Auger,et al.  Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions , 2009 .

[8]  Ioannis G. Tsoulos,et al.  Solving constrained optimization problems using a novel genetic algorithm , 2009, Appl. Math. Comput..

[9]  P. Subbaraj,et al.  Enhancement of Self-adaptive real-coded genetic algorithm using Taguchi method for Economic dispatch problem , 2011, Appl. Soft Comput..

[10]  W. Shao,et al.  Improved self-adaptive genetic algorithm with quantum scheme for electromagnetic optimisation , 2014 .

[11]  Gilbert Syswerda,et al.  Uniform Crossover in Genetic Algorithms , 1989, ICGA.

[12]  Li Li,et al.  Self-Adaptive Genetic Algorithm for LTE Backhaul Network , 2014, J. Networks.

[13]  Mohamed Kurdi,et al.  A new hybrid island model genetic algorithm for job shop scheduling problem , 2015, Comput. Ind. Eng..

[14]  G. Andal Jayalakshmi,et al.  A Hybrid Genetic Algorithm - A New Approach to Solve Traveling Salesman Problem , 2001, Int. J. Comput. Eng. Sci..

[15]  Kusum Deep,et al.  A self-organizing migrating genetic algorithm for constrained optimization , 2008, Appl. Math. Comput..

[16]  Wei Gao Study on New Improved Hybrid Genetic Algorithm , 2012 .

[17]  Marin Golub,et al.  On the recombination operator in the real-coded genetic algorithms , 2013, 2013 IEEE Congress on Evolutionary Computation.

[18]  Qingfu Zhang,et al.  Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters , 2011, IEEE Transactions on Evolutionary Computation.

[19]  Janez Brest,et al.  Population size reduction for the differential evolution algorithm , 2008, Applied Intelligence.

[20]  K. Deb An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[21]  Aizhu Zhang,et al.  A Hybrid Genetic Algorithm and Gravitational Search Algorithm for Global Optimization , 2015 .

[22]  Cai Zi-Xing,et al.  Hybrid Self-Adaptive Orthogonal Genetic Algorithm for Solving Global Optimization Problems , 2010 .

[23]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[24]  Ruhul A. Sarker,et al.  GA with a new multi-parent crossover for solving IEEE-CEC2011 competition problems , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[25]  Kalyanmoy Deb,et al.  Self-Adaptive Genetic Algorithms with Simulated Binary Crossover , 2001, Evolutionary Computation.

[26]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[27]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[28]  Olli Nevalainen,et al.  Self-Adaptive Genetic Algorithm for Clustering , 2003, J. Heuristics.

[29]  Jui-Yu Wu,et al.  Real-Coded Genetic Algorithm for Solving Generalized Polynomial Programming Problems , 2007, J. Adv. Comput. Intell. Intell. Informatics.

[30]  Mehmet Fatih Tasgetiren,et al.  Differential evolution algorithm with ensemble of parameters and mutation strategies , 2011, Appl. Soft Comput..

[31]  L. Darrell Whitley,et al.  Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator , 1989, International Conference on Genetic Algorithms.

[32]  Yong Wang,et al.  Hybrid Self-Adaptive Orthogonal Genetic Algorithm for Solving Global Optimization Problems: Hybrid Self-Adaptive Orthogonal Genetic Algorithm for Solving Global Optimization Problems , 2010 .

[33]  Nguyen Xuan Hoai,et al.  A new hybrid Genetic Algorithm for solving the Bounded Diameter Minimum Spanning Tree problem , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[34]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[35]  Alden H. Wright,et al.  Genetic Algorithms for Real Parameter Optimization , 1990, FOGA.

[36]  M. Yamamura,et al.  Multi-parent recombination with simplex crossover in real coded genetic algorithms , 1999 .

[37]  Wojdan Alsaeedan,et al.  A Self-adaptive Genetic Algorithm for the Word Sense Disambiguation Problem , 2015, IEA/AIE.

[38]  Kalyanmoy Deb,et al.  A Computationally Efficient Evolutionary Algorithm for Real-Parameter Optimization , 2002, Evolutionary Computation.

[39]  Sylvain Delisle,et al.  A meta-learning system based on genetic algorithms , 2004, SPIE Defense + Commercial Sensing.

[40]  Alex S. Fukunaga,et al.  Improving the search performance of SHADE using linear population size reduction , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[41]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[42]  Xishan Wen,et al.  A self-adaptive genetic algorithm to estimate JA model parameters considering minor loops , 2015 .

[43]  César Hervás-Martínez,et al.  Improving crossover operator for real-coded genetic algorithms using virtual parents , 2007, J. Heuristics.

[44]  Shigenobu Kobayashi,et al.  A real-coded genetic algorithm using the unimodal normal distribution crossover , 2003 .

[45]  Alex S. Fukunaga,et al.  Success-history based parameter adaptation for Differential Evolution , 2013, 2013 IEEE Congress on Evolutionary Computation.

[46]  Yiwen Wang,et al.  A new hybrid genetic algorithm based on chaos and PSO , 2009, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[47]  M. M. Ali,et al.  A numerical study of some modified differential evolution algorithms , 2006, Eur. J. Oper. Res..

[48]  Ruhul A. Sarker,et al.  A new genetic algorithm for solving optimization problems , 2014, Eng. Appl. Artif. Intell..

[49]  Robert G. Reynolds,et al.  CULTURAL ALGORITHMS: COMPUTATIONAL MODELING OF HOW CULTURES LEARN TO SOLVE PROBLEMS: AN ENGINEERING EXAMPLE , 2005, Cybern. Syst..

[50]  Ponnuthurai Nagaratnam Suganthan,et al.  Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization , 2014 .

[51]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[52]  M. Senthil Arumugam,et al.  New hybrid genetic operators for real coded genetic algorithm to compute optimal control of a class of hybrid systems , 2005, Appl. Soft Comput..

[53]  A. Kai Qin,et al.  Self-adaptive differential evolution algorithm for numerical optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[54]  Kalyanmoy Deb,et al.  Non-Uniform Mapping in Binary-Coded Genetic Algorithms , 2014, BIC-TA.