Afonso C. C. Lemonge

A new adaptive penalty scheme for genetic algorithms

A parameter-less adaptive penalty scheme for genetic algorithms applied to constrained optimization problems is proposed. The performance of this new scheme is examined using test problems from the evolutionary computation literature as well as structural engineering constrained optimization problems.

A hybrid genetic algorithm for constrained optimization problems in mechanical engineering

A genetic algorithm (GA) is hybridized with an artificial immune system (AIS) as an alternative to tackle constrained optimization problems in engineering. The AIS is inspired in the clonal selection principle and is embedded into a standard GA search engine in order to help move the population into the feasible region. The procedure is applied to mechanical engineering problems available in the literature and compared to other alternative techniques.

A new hybrid AIS-GA for constrained optimization problems in mechanical engineering

A genetic algorithm (GA) is hybridized with an artificial immune system (AIS) as an alternative to tackle constrained optimization problems in engineering. The AIS is inspired in the clonal selection principle and is embedded into a standard GA search engine in order to help move the population into the feasible region. The procedure is applied to mechanical engineering problems available in the literature and compared to other alternative techniques.

A genetic algorithm encoding for a class of cardinality constraints

A genetic algorithm encoding is proposed which is able to automatically satisfy a class of important cardinality constraints where the set of distinct values of the design variables must be a subset--of cardinality not exceeding a given value--of a larger set of available items.The solution of the practically important structural optimization problem where the set of distinct values of the design variables must be a small subset of a larger set of commercially available values is used as a test-bed. Very good results have been found in the numerical experiments performed using standard binary encoding and genetic operators.

A study on fitness inheritance for enhanced efficiency in real-coded genetic algorithms

This paper presents a study on the use of fitness inheritance as a surrogate model to assist a genetic algorithm (GA) in solving optimization problems with a limited computational budget.We compared the impact to the evolutionary search introducing three surrogate models: (i) averaged inheritance, (ii) weighted inheritance and (iii) parental inheritance. Numerical experiments are performed in order to assess the applicability and the performance of the proposed approach. The results show that when using a fixed reduced budget of expensive simulations, the surrogate-assisted genetic algorithm allows for improving the final solutions when compared to the standard GA. We find that the averaged and parental inheritance are more effective when compared to weighted inheritance, and they are recommended for expensive of optimization problems using GA-based search.

On Similarity-Based Surrogate Models for Expensive Single- and Multi-objective Evolutionary Optimization

In this chapter we propose a surrogate-assisted framework for expensive single- and multi-objective evolutionary optimization, under a fixed budget of computationally intensive evaluations. The framework uses similarity-based surrogate models and an individual-based model management with pre-selection. Instead of existing frameworks where the surrogates are used to improve the performance of evolutionary operators or as local search tools, here we use them to allow for an augmented number of generations to evolve solutions. The introduction of the surrogates into the evolutionary cycle is controlled by a single parameter, which is related with the number of generations performed by the evolutionary algorithm.Numerical experiments are conducted in order to assess the applicability and the performance in constrained and unconstrained, single- and multi-objective optimization problems. The results show that the present framework arises as an attractive alternative to improve the final solutions with a fixed budget of expensive evaluations.

A Critical Review of Adaptive Penalty Techniques in Evolutionary Computation

Constrained optimization problems are common in the sciences, engineering, and economics. Due to the growing complexity of the problems tackled, nature-inspired metaheuristics in general, and evolutionary algorithms in particular, are becoming increasingly popular. As move operators (recombination and mutation) are usually blind to the constraints, most metaheuristics must be equipped with a constraint handling technique. Although conceptually simple, penalty techniques usually require user-defined problem-dependent parameters, which often significantly impact the performance of a metaheuristic. A penalty technique is said to be adaptive when it automatically sets the values of all parameters involved using feedback from the search process without user intervention. This chapter presents a survey of the most relevant adaptive penalty techniques from the literature, identifies the main concepts used in the adaptation process, as well as observed shortcomings, and suggests further work in order to increase the understanding of such techniques.

An adaptive penalty scheme for steady-state genetic algorithms

A parameter-less adaptive penalty scheme for steady-state genetic algorithms applied to constrained optimization problems is proposed. For each constraint, a penalty parameter is adaptively computed along the run according to information extracted from the current population such as the existence of feasible individuals and the level of violation of each constraint. Using real coding, rank-based selection, and operators available in the literature, very good results are obtained.

Variants of an adaptive penalty scheme for steady-state genetic algorithms in engineering optimization

Purpose – The purpose of this paper is to propose variants of an adaptive penalty scheme for steady-state genetic algorithms applied to constrained engineering optimization problems. Design/methodology/approach – For each constraint a penalty parameter is adaptively computed along the evolution according to information extracted from the current population such as the existence of feasible individuals and the level of violation of each constraint. The adaptive penalty method (APM), as originally proposed, computes the constraint violations of the initial population, and updates the penalty coefficient of each constraint after a given number of new individuals are inserted in the population. A second variant, called sporadic APM with constraint violation accumulation, works by accumulating the constraint violations during a given insertion of new offspring into the population, updating the penalty coefficients, and fixing the penalty coefficients for the next generations. The APM with monotonic penalty coe...

A similarity-based surrogate model for expensive evolutionary optimization with fixed budget of simulations

In order to find a satisfactory solution, genetic algorithms, in spite of their ability to solve difficult optimization problems, usually require a large number of fitness evaluations. When expensive simulations are required, using genetic algorithms as optimization tools can become prohibitive.