Genetic Algorithm: Theory, Literature Review, and Application in Image Reconstruction

Genetic Algorithm (GA) is one of the most well-regarded evolutionary algorithms in the history. This algorithm mimics Darwinian theory of survival of the fittest in nature. This chapter presents the most fundamental concepts, operators, and mathematical models of this algorithm. The most popular improvements in the main component of this algorithm (selection, crossover, and mutation) are given too. The chapter also investigates the application of this technique in the field of image processing. In fact, the GA algorithm is employed to reconstruct a binary image from a completely random image.

[1]  R. Hinterding,et al.  Gaussian mutation and self-adaption for numeric genetic algorithms , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[2]  Michael L. Mauldin,et al.  Maintaining Diversity in Genetic Search , 1984, AAAI.

[3]  Erick Cantú-Paz,et al.  A Survey of Parallel Genetic Algorithms , 2000 .

[4]  Lalit M. Patnaik,et al.  Genetic algorithms: a survey , 1994, Computer.

[5]  Hisao Ishibuchi,et al.  Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining , 2004, Fuzzy Sets Syst..

[6]  Marcus Hutter,et al.  Fitness uniform selection to preserve genetic diversity , 2001, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[7]  David E. Goldberg,et al.  A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-Oriented Simulated Annealing , 1990, Complex Syst..

[8]  Eugene Semenkin,et al.  Self-configuring Genetic Algorithm with Modified Uniform Crossover Operator , 2012, ICSI.

[9]  D. J. Smith,et al.  A Study of Permutation Crossover Operators on the Traveling Salesman Problem , 1987, ICGA.

[10]  Lawrence Davis,et al.  Applying Adaptive Algorithms to Epistatic Domains , 1985, IJCAI.

[11]  G. Deon Oosthuizen.  SUPERGRAN: A Connectionist Approach to Learning, Integrating Genetic Algorithms and Graph Induction , 1987, ICGA.

[12]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

[13]  Kusum Deep,et al.  A new mutation operator for real coded genetic algorithms , 2007, Appl. Math. Comput..

[14]  David E. Goldberg,et al.  Genetic Algorithms, Tournament Selection, and the Effects of Noise , 1995, Complex Syst..

[15]  Xiao-Bing Hu,et al.  An efficient Genetic Algorithm with uniform crossover for the multi-objective Airport Gate Assignment Problem , 2007 .

[16]  Gilbert Syswerda,et al.  A Study of Reproduction in Generational and Steady State Genetic Algorithms , 1990, FOGA.

[17]  Carol A. Ankenbrandt.  An Extension to the Theory of Convergence and a Proof of the Time Complexity of Genetic Algorithms , 1990, FOGA.

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

[19]  John J. Grefenstette,et al.  Genetic Algorithms for the Traveling Salesman Problem , 1985, ICGA.

[20]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[21]  G. Syswerda,et al.  Schedule Optimization Using Genetic Algorithms , 1991 .

[22]  John J. Grefenstette,et al.  How Genetic Algorithms Work: A Critical Look at Implicit Parallelism , 1989, ICGA.

[23]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[24]  Chang Wook Ahn,et al.  Elitism-based compact genetic algorithms , 2003, IEEE Trans. Evol. Comput..

[25]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[26]  Larry J. Eshelman,et al.  Biases in the Crossover Landscape , 1989, ICGA.

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

[28]  Lothar Thiele,et al.  A Comparison of Selection Schemes Used in Evolutionary Algorithms , 1996, Evolutionary Computation.

[29]  Rakesh Kumar,et al.  Blending Roulette Wheel Selection & Rank Selection in Genetic Algorithms , 2012 .

[30]  Chai Tianyou,et al.  Survey on Genetic Algorithm , 1996 .

[31]  Shigeyoshi Tsutsui,et al.  Forking Genetic Algorithm with Blocking and Shrinking Modes (fGA) , 1993, ICGA.

[32]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[33]  David B. Fogel,et al.  Evolution-ary Computation 1: Basic Algorithms and Operators , 2000 .

[34]  David R. Jefferson,et al.  Selection in Massively Parallel Genetic Algorithms , 1991, ICGA.

[35]  Sushil J. Louis,et al.  Designer Genetic Algorithms: Genetic Algorithms in Structure Design , 1991, ICGA.

[36]  A. Neubauer,et al.  A theoretical analysis of the non-uniform mutation operator for the modified genetic algorithm , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).