A Jumping Gene Paradigm for Evolutionary Multiobjective Optimization

A new evolutionary computing algorithm on the basis of the ldquojumping genesrdquo (JG) phenomenon is proposed in this paper. It emulates the gene transposition in the genome that was discovered by Nobel Laureate, Barbara McClintock, in her work on the corn plants. The principle of JGs that is adopted for evolutionary computing is outlined. The procedures for executing the computational optimization are provided. A large number of constrained and unconstrained test functions have been utilized to verify this new scheme. Its performances on convergence and diversity have been statistically examined and comparisons with other evolutionary algorithms are carried out. It has been discovered that this new scheme is robust and able to provide outcomes quickly and accurately. A stringent measure of binary-indicator is also applied for algorithm classification. The outcome from this test indicates that the JG paradigm is a very competitive scheme for multiobjective optimization and also a compatible evolutionary computing scheme when speed in convergence, diversity, and accuracy are simultaneously required.

[1]  Santosh K. Gupta,et al.  Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using genetic algorithm (GA) with the jumping genes operator , 2003, Comput. Chem. Eng..

[2]  J. Carel von Vaupel Klein,et al.  Acquiring genomes. A theory of the origins of species , 2008 .

[3]  Andrew P. Read.  The Dynamic Genome - Barbara McClintock's Ideas in the Century of Genetics , 1993 .

[4]  Ashok D. Belegundu,et al.  Multi-objective optimization of laminated ceramic composites using genetic algorithms , 1994 .

[5]  Kim-Fung Man,et al.  A Jumping Gene Algorithm for Multiobjective Resource Management in Wideband CDMA Systems , 2005, Comput. J..

[6]  David Corne,et al.  The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[7]  T. T. Binh.  MOBES : A multiobjective evolution strategy for constrained optimization problems , 1997 .

[8]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[9]  E. Costa,et al.  Enhancing transposition performance , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[10]  Peter J. Fleming,et al.  Multiobjective optimization and multiple constraint handling with evolutionary algorithms. II. Application example , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[11]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[12]  Alexander V. Spirov,et al.  Jumping Genes-mutators Can Rise Efficacy Of Evolutionary Search , 2002, GECCO.

[13]  Carlo Poloni,et al.  Hybrid GA for Multi Objective Aerodynamic Shape Optimisation , 1995 .

[14]  Gary B. Lamont,et al.  Multiobjective evolutionary algorithms: classifications, analyses, and new innovations , 1999 .

[15]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[16]  Eckart Zitzler,et al.  Evolutionary algorithms for multiobjective optimization: methods and applications , 1999 .

[17]  C. Coello,et al.  Multiobjective optimization using a micro-genetic algorithm , 2001 .

[18]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[19]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[20]  David Botstein,et al.  McClintock Myths. (Book Reviews: The Dynamic Genome. Barbara McClintock's Ideas in the Century of Genetics.) , 1992 .

[21]  Peter J. Fleming,et al.  On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers , 1996, PPSN.

[22]  Lothar Thiele,et al.  Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.

[23]  B. Mcclintock.  The origin and behavior of mutable loci in maize , 1950, Proceedings of the National Academy of Sciences.

[24]  V. Corces,et al.  Transposable element-host interactions: regulation of insertion and excision. , 1997, Annual review of genetics.

[25]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[26]  Jeffrey Horn,et al.  The Niched Pareto Genetic Algorithm 2 Applied to the Design of Groundwater Remediation Systems , 2001, EMO.

[27]  Masahiro Tanaka,et al.  GA-based decision support system for multicriteria optimization , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[28]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[29]  Lynn Helena Caporale.  Darwin in the genome : molecular strategies in biological evolution , 2003 .

[30]  B. Mcclintock,et al.  Chromosome organization and genic expression. , 1951, Cold Spring Harbor symposia on quantitative biology.

[31]  Carlos A. Coello Coello,et al.  A Micro-Genetic Algorithm for Multiobjective Optimization , 2001, EMO.

[32]  S.H. Sun,et al.  An Optimized Wideband Quarter-Wave Patch Antenna Design , 2005, IEEE Antennas and Wireless Propagation Letters.

[33]  Peter J. Fleming,et al.  Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[34]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..

[35]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[36]  John F. McDonald,et al.  Molecular domestication of mobile elements , 2004, Genetica.

[37]  C. Fonseca,et al.  GENETIC ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION: FORMULATION, DISCUSSION, AND GENERALIZATION , 1993 .