TREATING CONSTRAINTS AS OBJECTIVES FOR SINGLE-OBJECTIVE EVOLUTIONARY OPTIMIZATION

By Carlos Artemio Coello Coello. 2000

302 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/ed27fa58d0675fca3fda1faf005b1eac8bbcc75d

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Citations in the corpus

CONSTRAINT-HANDLING USING AN EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION TECHNIQUE (2000, methodology): “Coello [10] proposed the use of a population-based multiobjective optimization technique such as VEGA [43] to handle each of the constraints of a singleobjective optimization problem as an objective.”
Evolutionary computation and structural design: A survey of the state-of-the-art (2005, methodology): “Multiobjective optimization methods in which an original single-objective problem is transformed into a multiobjective one by treating all constraints in the original problem as objectives in the transformed problem [135-139].”
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art (2000, methodology): “This approach used an MOEA to solve a heavily constrained single-objective optimization problem (Coello, 2000).”
A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization (2006, methodology): “Coello [19] used a population-based multiobjective technique such as Vector Evaluated Genetic Algorithm (VEGA) [20] to treat each constraint as an objective.”
Constraint-handling in genetic algorithms through the use of dominance-based tournament selection (2002, methodology): “Coello [7] proposed the use of a population-based multiobjective optimization technique similar to VEGA [33] to handle each of the constraints of a single-objective optimization problem as an objective.”
Constraint-handling in nature-inspired numerical optimization: Past, present and future (2011, methodology): “Landa and Coello [75] also adopted the feasibility rules in an approach in which a cultural DE-based mechanism was developed, with the aim of incorporating knowledge from the problem into the search process when solving CNOPs.”
Self-adaptive fitness formulation for constrained optimization (2003, methodology): “Coello [3] eliminated some of the drawbacks of the Parmee and Purchase [20].”
An improved particle swarm optimizer for mechanical design optimization problems (2004, methodology): “This problem was dealt with by Coello Coello [34] using GA with a dominance-based tournament selection scheme (GADTS) to handle constraints.”