The EMOO repository: a resource for doing research in evolutionary multiobjective optimization

This article briefly describes the evolutionary multi-objective optimization (EMOO) repository, which has become much more than the simple list of bibliographic references that originated it. In its current state, the EMOO repository contains many Web resources, including Ph.D. theses, software, contact information of EMOO researchers and information about EMOO-related events. Such information has become a valuable source for students and researchers interested in this area.

[1]  Graham Kendall,et al.  An evolutionary approach for the tuning of a chess evaluation function using population dynamics , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[2]  David B. Fogel,et al.  Evolutionary Computation: The Fossil Record , 1998 .

[3]  Nils Aall Barricelli,et al.  Numerical testing of evolution theories , 1963 .

[4]  Robert Axelrod,et al.  The Evolution of Strategies in the Iterated Prisoner's Dilemma , 2001 .

[5]  Carlos A. Coello Coello,et al.  Solving Multiobjective Optimization Problems Using an Artificial Immune System , 2005, Genetic Programming and Evolvable Machines.

[6]  D.B. Fogel,et al.  A self-learning evolutionary chess program , 2004, Proceedings of the IEEE.

[7]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[8]  David B. Fogel,et al.  Unearthing a Fossil from the History of Evolutionary Computation , 1998, Fundam. Informaticae.

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

[10]  George H. Burgin,et al.  COMPETITIVE GOAL-SEEKING THROUGH EVOLUTIONARY?PROGRAMMING. , 1969 .

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

[12]  J. Reed,et al.  Simulation of biological evolution and machine learning. I. Selection of self-reproducing numeric patterns by data processing machines, effects of hereditary control, mutation type and crossing. , 1967, Journal of theoretical biology.

[13]  David B. Fogel,et al.  Further Evolution of a Self-Learning Chess Program , 2005, CIG.

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

[15]  Moshe Sipper,et al.  GP-EndChess: Using Genetic Programming to Evolve Chess Endgame Players , 2005, EuroGP.

[16]  W. Daniel Hillis,et al.  Co-evolving parasites improve simulated evolution as an optimization procedure , 1990 .