C. Coello

Publications
Influence

Evolutionary Algorithms for Solving Multi-Objective Problems

C. Coello, D. V. Veldhuizen, G. Lamont
Computer Science
Genetic Algorithms and Evolutionary Computation
30 June 2002

MOEA. 1. Preface. 2. Foreword. 3. Appendix A: MOEA Classification and Technique Analysis. 4. Appendix B: MOPs in the Literature. 5. Appendix C: Ptrue & PFtrue for Selected Numeric Mops. 6. Appendix D: P true & PF true for Side-Constrained MOP. 7. Appendix E:MOEA Software Availability. 8. Appendix F: MOAE-Related Information.List of Figures. Expand

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Handling multiple objectives with particle swarm optimization

C. Coello, G. Pulido, M. S. Lechuga
Mathematics, Computer Science
IEEE Transactions on Evolutionary Computation
1 June 2004

This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions. Expand

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Use of a self-adaptive penalty approach for engineering optimization problems

C. Coello
Computer Science
1 March 2000

This paper introduces the notion of using co-evolution to adapt the penalty factors of a fitness function incorporated in a genetic algorithm (GA) for numerical optimization. Expand

SMPSO: A new PSO-based metaheuristic for multi-objective optimization

Antonio J. Nebro, J. J. Durillo, J. García-Nieto, C. Coello, F. Luna, E. Alba
Mathematics, Computer Science
IEEE Symposium on Computational Intelligence in…
15 May 2009

We present a new multi-objective particle swarm optimization algorithm (PSO) characterized by the use of a strategy to limit the velocity of the particles. Expand

Problem Deflnitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-Parameter Optimization

J. Liang, T. P. Runarsson, +4 authors K. Deb
Computer Science
2006

Constraint-handling in genetic algorithms through the use of dominance-based tournament selection

C. Coello, E. Mezura-Montes
Mathematics, Computer Science
Adv. Eng. Informatics
1 July 2002

We propose a dominance-based selection scheme to incorporate constraints into the fitness function of a genetic algorithm used for global optimization. Expand

Improving PSO-based Multi-Objective Optimization using Crowding , Mutation and �-Dominance

M. Sierra, C. Coello
2005

In this paper, we propose a new Multi-Objective Particle Swarm Optimizer, which is based on Pareto dominance and the use of a crowding factor to filter out the list of available leaders. We also… Expand

A simple multimembered evolution strategy to solve constrained optimization problems

E. Mezura-Montes, C. Coello
Computer Science, Mathematics
IEEE Transactions on Evolutionary Computation
1 February 2005

This work presents a simple multimembered evolution strategy to solve global nonlinear optimization problems using a simple diversity mechanism based on allowing infeasible solutions to remain in the population. Expand

Solving Multiobjective Optimization Problems Using an Artificial Immune System

C. Coello, N. C. Cortés
Computer Science
Genetic Programming and Evolvable Machines
1 June 2005

We propose an algorithm based on the clonal selection principle to solve multiobjective optimization problems (either constrained or unconstrained). Expand

Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)

C. Coello, G. Lamont, D. V. Veldhuizen
Computer Science
1 December 2006

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