Evolutionary programming with a simulated-conformist mutation strategy

Evolutionary programming has been widely implemented as a continuous optimization algorithm. Prior studies have come to a bottleneck because most of the evolutionary programming algorithms are unable to robustly solve different types of optimization problems. We argue that such a bottleneck results from the existing mutation strategies’ making little use of the population information. Inspired by a psychological model which describes how a person optimizes his/her social activities by conformity behavior, this study proposes a variation vector of the mutation to simulate the conformity behavior with behavior-reference, majority-impact, and distinctive-impact factors. These factors, respectively, correspond with three types of population information for each mutated individual: heuristic information, optimal gradient, and population diversity. We use the proposed vector to design an improved evolutionary programming with a simulated-conformist mutation strategy. The results show that the population information produced by the three factors enhance the robustness of the performance of evolutionary programming in solving both uni- and multimodal functions. The finding is verified by empirical analyses of two sets of benchmark functions proposed in 1998 and 2013. The numerical results indicate that the proposed algorithm performs significantly better on average than the existing EPs and some other algorithms with similar strategies.

[1]  Nikolaus Hansen,et al.  The CMA Evolution Strategy: A Comparing Review , 2006, Towards a New Evolutionary Computation.

[2]  Thomas Bäck,et al.  An Overview of Evolutionary Algorithms for Parameter Optimization , 1993, Evolutionary Computation.

[3]  Javad Poshtan,et al.  Momentum coefficient for promoting accuracy and convergence speed of evolutionary programming , 2012, Appl. Soft Comput..

[4]  Peter J. Fleming,et al.  The Stud GA: A Mini Revolution? , 1998, PPSN.

[5]  Wei Hou,et al.  Evolutionary programming using a mixed mutation strategy , 2007, Inf. Sci..

[6]  Hans-Paul Schwefel,et al.  Evolution and Optimum Seeking: The Sixth Generation , 1993 .

[7]  Xin Yao,et al.  Recurring Two-Stage Evolutionary Programming: A Novel Approach for Numeric Optimization , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[8]  Yoed Tsur,et al.  Harnessing evolutionary programming for impedance spectroscopy analysis: A case study of mixed ionic-electronic conductors , 2011 .

[9]  David B. Fogel,et al.  System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling , 1991 .

[10]  Ponnuthurai N. Suganthan,et al.  Ensemble strategies with adaptive evolutionary programming , 2010, Inf. Sci..

[11]  Xin Yao,et al.  Diversity Guided Evolutionary Programming: A novel approach for continuous optimization , 2012, Appl. Soft Comput..

[12]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[13]  Libin Hong,et al.  A step size based self-adaptive mutation operator for evolutionary programming , 2014, GECCO.

[14]  James Kennedy,et al.  Defining a Standard for Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[15]  Ponnuthurai N. Suganthan,et al.  Self-adaptive differential evolution with multi-trajectory search for large-scale optimization , 2011, Soft Comput..

[16]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[17]  C. Christober Asir Rajan.  Hydro-thermal unit commitment problem using simulated annealing embedded evolutionary programming approach , 2011 .

[18]  Sung Hoon Jung,et al.  Queen-bee evolution for genetic algorithms , 2003 .

[19]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[20]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

[21]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[22]  Grzegorz Rozenberg,et al.  Handbook of Natural Computing , 2011, Springer Berlin Heidelberg.

[23]  A. A. El-Keib,et al.  A fuzzy evolutionary programming-based solution methodology for security-constrained generation maintenance scheduling , 2003 .

[24]  P. K. Chattopadhyay,et al.  Evolutionary programming techniques for economic load dispatch , 2003, IEEE Trans. Evol. Comput..

[25]  Wei Hou,et al.  A fuzzy clustering algorithm based on evolutionary programming , 2009, Expert Syst. Appl..

[26]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[27]  Xianhua Tang,et al.  On existence of periodic solutions of Rayleigh equation of retarded type , 2007 .

[28]  Xiao-Shan Gao,et al.  Evolutionary programming based on non-uniform mutation , 2007, Appl. Math. Comput..

[29]  Qing He,et al.  An improved FCMBP fuzzy clustering method based on evolutionary programming , 2011, Comput. Math. Appl..

[30]  Yew-Soon Ong,et al.  A Probabilistic Memetic Framework , 2009, IEEE Transactions on Evolutionary Computation.

[31]  Kumar Chellapilla,et al.  Combining mutation operators in evolutionary programming , 1998, IEEE Trans. Evol. Comput..

[32]  Petros Koumoutsakos,et al.  Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) , 2003, Evolutionary Computation.

[33]  Takeshi Nagata,et al.  A Solution for Unit Commitment Using Lagrangian Relaxation Combined with Evolutionary Programming , 1996 .

[34]  D. Fogel.  Applying evolutionary programming to selected traveling salesman problems , 1993 .

[35]  Gang Chen,et al.  Preserving and Exploiting Genetic Diversity in Evolutionary Programming Algorithms , 2009, IEEE Transactions on Evolutionary Computation.

[36]  S. Asch.  Studies of independence and conformity: I. A minority of one against a unanimous majority. , 1956 .

[37]  Xin Yao,et al.  Scaling Up Evolutionary Programming Algorithms , 1998, Evolutionary Programming.

[38]  Jing Lu,et al.  Adaptive evolutionary programming based on reinforcement learning , 2008, Inf. Sci..

[39]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[40]  David B. Fogel,et al.  Evolving artificial intelligence , 1992 .

[41]  Xianzhong Duan,et al.  Hybrid algorithm of differential evolution and evolutionary programming for optimal reactive power flow , 2010 .

[42]  Xin Yao,et al.  Adapting Self-Adaptive Parameters in Evolutionary Algorithms , 2001, Applied Intelligence.

[43]  Md. Tanvir Alam Anik,et al.  A dual mutation strategy embedded Evolutionary Programming for continuous optimization , 2013, 2013 World Congress on Nature and Biologically Inspired Computing.

[44]  Xin Yao,et al.  Evolutionary programming using mutations based on the Levy probability distribution , 2004, IEEE Transactions on Evolutionary Computation.

[45]  Masao Fukushima,et al.  Directed Evolutionary Programming: Towards an Improved Performance of Evolutionary Programming , 2006, 2006 IEEE International Conference on Evolutionary Computation.