The application of evolutionary algorithms (EAs) in multiobjective optimization is currently receiving growing interest from researchers with various backgrounds. Most research in this area has understandably concentrated on the selection stage of EAs, due to the need to integrate vectorial performance measures with the inherently scalar way in which EAs reward individual performance, i.e., number of offspring. In this review, current multiobjective evolutionary approaches are discussed, ranging from the conventional analytical aggregation of the different objectives into a single function to a number of populationbased approaches and the more recent ranking schemes based on the definition of Pareto-optimality. The sensitivity of different methods to

Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade, a variety of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define multiobjective optimization problems and certain related concepts, present an MOEA classification scheme, and evaluate the variety of contemporary MOEAs. Current MOEA theoretical developments are evaluated; specific topics addressed include fitness functions, Pareto ranking, niching, fitness sharing, mating restriction, and secondary populations. Since the development and application of MOEAs is a dynamic and rapidly growing activity, we focus on key analytical insights based upon critical MOEA evaluation of c...

. This paper presents a critical review of the most important evolutionary-based multiobjective optimization techniques developed over the years, emphasizing the importance of analyzing their Operations Research roots as a way to motivate the development of new approaches that exploit the search capabilities of evolutionary algorithms. Each technique is briefly described mentioning its advantages and disadvantages, their degree of applicability and some of their known applications. Finally, the future trends in this discipline and some of the open areas of research are also addressed. Keywords: multiobjective optimization, multicriteria optimization, vector optimization, genetic algorithms, evolutionary algorithms, artificial intelligence. 1 Introduction Since the pioneer work of Rosenberg in the late 60s regarding the possibility of using genetic-based search to deal with multiple objectives, this new area of research (now called evolutionary multiobjective optimization) has grown c...

Evolutionary computation is emerging as a new engineering computational paradigm, which may significantly change the present structural design practice. For this reason, an extensive study of evolutionary computation in the context of structural design has been conducted in the Information Technology and Engineering School at George Mason University and its results are reported here. First, a general introduction to evolutionary computation is presented and recent developments in this field are briefly described. Next, the field of evolutionary design is introduced and its relevance to structural design is explained. Further, the issue of creativity/novelty is discussed and possible ways of achieving it during a structural design process are suggested. Current research progress in building engineering systems ’ representations, one of the key issues in evolutionary design, is subsequently discussed. Next, recent developments in constraint-handling methods in evolutionary optimization are reported. Further, the rapidly growing field of evolutionary multiobjective optimization is presented and briefly described. An emerging subfield of coevolutionary design is subsequently introduced and its current advancements reported. Next, a comprehensive review of the applications of evolutionary computation in structural design is provided and chronologically classified. Finally, a summary of the current research status and a discussion on the most promising paths of future research are also presented.

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Genetic algorithms (GAs) are stochastic search techniques inspired by the principles of natural selection and natural genetics which have revealed a number of characteristics particularly useful for applications in optimization, engineering, and computer science, among other fields. In control engineering, they have found application mainly in problems involving functions difficult to characterize mathematically or known to present difficulties to more conventional numerical optimizers, as well as problems involving non-numeric and mixed-type variables. In addition, they exhibit a large degree of parallelism, making it possible to effectively exploit the computing power made available through parallel processing. Despite their early recognized potential for multiobjective optimization (almost all engineering problems involve multiple, often conflicting objectives), genetic algorithms have, for the most part, been applied to aggregations of the objectives in a single-objective fashion, like conventional optimizers. Although alternative approaches based on the notion of Pareto-dominance have been suggested, multiobjective optimization with genetic algorithms has received comparatively

Applying evolutionary computation (EC) to multicriteria decision making addresses two difficult problems: (1) searching intractably large and complex spaces and (2) deciding among multiple objectives. Both of these problems are open areas of research, but relatively little work has been done on the COMBINED problem of searching large spaces to meet multiple objectives. While multicriteria decision analysis usually assumes a small number of alternative solutions to choose from, or an "easy" (e.g., linear) space to search, research on robust search methods generally assumes some way of aggregating multiple objectives into a single figure of merit. This traditional separation of search and multicriteria decisions allows for two straightforward hybrid strategies: (1) make multicriteria decisions FIRST, to aggregate objectives, then apply EC search to optimize the resulting figure of merit, or (2) conduct multiple EC searches FIRST using different aggregations of the objectives in order to o...

This paper presents a new methodology for identifying cost-effective sampling plans to reduce costs at sites with numerous existing monitoring wells.

A management model has been developed that predicts human health risk and uses a noisy genetic algorithm to identify promising risk-based corrective action designs [Smalley et al, 2000]. Noisy genetic algorithms are ordinary genetic algorithms that operate in noisy environments. The “noise ” can be defined as any factor that hinders the accurate evaluation of the fitness of a given trial design. The noisy genetic algorithm uses a type of noisy fitness function called the sampling fitness function, which utilizes sampling in order to reduce the amount of noise from fitness evaluations in noisy environments. This Monte-Carlo-type sampling provides a more realistic estimate of the fitness as the design is exposed to a wide variety of conditions. Unlike Monte Carlo simulation modeling, however, the noisy genetic algorithm is highly efficient and can identify robust designs with only a few samples per design. For complex water resources and environmental engineering design problems with complex fitness functions, however, it is important that the sampling be as efficient as possible. In this paper, methods for reducing the computational effort through improved sampling techniques are investigated. A number of different sampling approaches will be presented and their performance compared using a case study of a risk-based corrective action design.

We propose a new solution to the problem of positioning base station transmitters of a mobile phone network and assigning frequencies to the transmitters, both in an optimal way. Since an exact solution cannot be expected to run in polynomial time for all interesting versions of this problem (they are all NP-hard), our algorithm follows a heuristic approach based on the evolutionary paradigm. For this evolution to be efficient, that is at the same time goal-oriented and sufficiently random, problem specific knowledge is embedded in the operators. The problem requires both the minimization of the cost and of the channel interference. We examine and compare two standard multiobjective techniques and a new algorithm, the steady state evolutionary algorithm with Pareto tournaments (stEAPT). One major finding of the empirical investigation is a strong influence of the choice of the multiobjective selection method on the utility of the problem-specific recombination leading to a significant difference in the solution quality. I.