Proceedings of the 9th annual conference on Genetic and evolutionary computation
A pheromone-rate-based analysis on the convergence time of ACO algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Constraint-handling techniques used with evolutionary algorithms
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Differential evolution in constrained numerical optimization: An empirical study
Information Sciences: an International Journal
Constraint-handling techniques used with evolutionary algorithms
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
An evolutionary computational model applied to cluster analysis of DNA microarray data
Expert Systems with Applications: An International Journal
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Although there are many evolutionary algorithms (EAs) for solving constrained optimization problems, there are few rigorous theoretical analyses. This paper presents a time complexity analysis of EAs for solving constrained optimization. It is shown when the penalty coefficient is chosen properly, direct comparison between pairs of solutions using penalty fitness function is equivalent to that using the criteria ldquosuperiority of feasible pointrdquo or ldquosuperiority of objective function value.rdquo This paper analyzes the role of penalty coefficients in EAs in terms of time complexity. The results show that in some examples, EAs benefit greatly from higher penalty coefficients, while in other examples, EAs benefit from lower penalty coefficients. This paper also investigates the runtime of EAs for solving the 0-1 knapsack problem and the results indicate that the mean first hitting times ranges from a polynomial-time to an exponential time when different penalty coefficients are used.