The Use of Opposition for Decreasing Function Evaluations in Population-Based Search

This chapter discusses the application of opposition-based computing to reducing the amount of function calls required to perform optimization by population-based search. We provide motivation and comparison to similar, but different approaches including antithetic variates and quasi-randomness/low-discrepancy sequences. We employ differential evolution and population-based incremental learning as optimization methods for image thresholding. Our results confirm improvements in required function calls, as well as support the oppositional princples used to attain them.

[1]  Shumeet Baluja,et al.  A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .

[2]  Shahryar Rahnamayan,et al.  Opposition-Based Differential Evolution Algorithms , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[3]  Harald Niederreiter,et al.  Random number generation and Quasi-Monte Carlo methods , 1992, CBMS-NSF regional conference series in applied mathematics.

[4]  Mario Ventresca,et al.  Simulated Annealing with Opposite Neighbors , 2007, 2007 IEEE Symposium on Foundations of Computational Intelligence.

[5]  Andries Petrus Engelbrecht,et al.  Fundamentals of Computational Swarm Intelligence , 2005 .

[6]  H.R. Tizhoosh,et al.  Opposition-Based Q(λ) Algorithm , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[7]  Shahryar Rahnamayan,et al.  Opposition-Based Differential Evolution for Optimization of Noisy Problems , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[8]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[9]  Mario Ventresca,et al.  A diversity maintaining population-based incremental learning algorithm , 2008, Inf. Sci..

[10]  Kar-Ann Toh.  Global Optimization by Monotonic Transformation , 2002, Comput. Optim. Appl..

[11]  Hamid R. Tizhoosh,et al.  Opposition-Based Reinforcement Learning , 2006, J. Adv. Comput. Intell. Intell. Informatics.

[12]  Mario Ventresca,et al.  Oppositional Concepts in Computational Intelligence , 2008, Oppositional Concepts in Computational Intelligence.

[13]  Zhi-You Wu,et al.  Convexification and Concavification for a General Class of Global Optimization Problems , 2005, J. Glob. Optim..

[14]  Mario Ventresca,et al.  Numerical condition of feedforward networks with opposite transfer functions , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[15]  M.M.A. Salama,et al.  Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.

[16]  Mohamed S. Kamel,et al.  Opposition-Based Q(lambda) Algorithm. , 2006, ISNN 2006.

[17]  Kaisa Miettinen,et al.  On initial populations of a genetic algorithm for continuous optimization problems , 2007, J. Glob. Optim..

[18]  Jonathan L. Shapiro,et al.  Diversity Loss in General Estimation of Distribution Algorithms , 2006, PPSN.

[19]  Mario Ventresca,et al.  Improving the Convergence of Backpropagation by Opposite Transfer Functions , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[20]  Hui Zhang,et al.  Image segmentation evaluation: A survey of unsupervised methods , 2008, Comput. Vis. Image Underst..

[21]  Michèle Sebag,et al.  Extending Population-Based Incremental Learning to Continuous Search Spaces , 1998, PPSN.

[22]  Marcus Randall,et al.  Anti-pheromone as a Tool for Better Exploration of Search Space , 2002, Ant Algorithms.

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

[24]  Thomas Bäck,et al.  Parallel Problem Solving from Nature — PPSN V , 1998, Lecture Notes in Computer Science.

[25]  Zhi-You Wu,et al.  A Novel monotonization Transformation for Some Classes of Global Optimization Problems , 2006, Asia Pac. J. Oper. Res..

[26]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

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

[28]  C. Lemieux.  Monte Carlo and Quasi-Monte Carlo Sampling , 2009 .

[29]  Terry S. Yoo,et al.  Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis , 2004 .

[30]  Mario Ventresca,et al.  Opposite Transfer Functions and Backpropagation Through Time , 2007, 2007 IEEE Symposium on Foundations of Computational Intelligence.

[31]  Reuven Y. Rubinstein,et al.  Monte Carlo Optimization, Simulation and Sensitivity of Queueing Networks , 1986 .

[32]  Azriel Rosenfeld,et al.  Threshold Evaluation Techniques , 1978, IEEE Transactions on Systems, Man, and Cybernetics.