Genetic algorithms (GAs) are powerful search techniques that are used successfully to solve problems in many different disciplines. Parallel GAs are particularly easy to implement and promise substantial gains in performance. As such, there has been extensive research in this field. This survey attempts to collect, organize, and present in a unified way some of the most representative publications on parallel genetic algorithms. To organize the literature, the paper presents a categorization of the techniques used to parallelize GAs, and shows examples of all of them. However, since the majority of the research in this field has concentrated on parallel GAs with multiple populations, the survey focuses on this type of algorithms. Also, the paper describes some of the most significant problems in modeling and designing multi-population parallel GAs and presents some recent advancements.

. Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations: selection, crossover and mutation. Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded genetic algorithms. Different models of genetic operators and some me...

This work analyzes some concepts of genetic algorithms and explains why they may be applied with success to some problems in function optimization. In addition to other performance properties, it has been shown that genetic algorithms are able to overcome local minima in highly multimodal functions (e.g., Rastrigin, Schwefel). The performance of genetic algorithms is supported by an extensive theory, which is based on the assumption of additive gene effects. But the current work shows that the assumption of additive gene effects is not weak, and that the dependence on specific parameter settings is much stronger than often believed. Furthermore, the assumptions regarding the fitness function are so restricting that slight modifications of the standard test functions cause a failure of the optimization procedure even though the function's structure is preserved. The current experiments focus on a few widely-used scalable test functions. the results indicate that a standard g...

The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We present an extension to previous categorizations of the parallelization techniques used in this field. We will use this categorization to guide us through a review of many of the most important publications. We will build on this survey to try to identify some of the problems that have not been studied systematically yet. 1 Introduction Genetic Algorithms (GAs) are efficient search methods based on principles of natural selection and population genetics. They are being successfully applied to problems in business, engineering and science (Goldberg, 1994). GAs use randomized operators operating over a population of candidate solutions to generate new points in the search space. In the past few years, parallel genetic algorithms (PGAs) have been used to solve difficult problems. Hard problems need a bigger population and this translates directly into higher computational costs. The basic...

We approach the difficult task of analyzing the complex behavior of even the simplest learning classifier system (LCS) by isolating one crucial subfunction in the LCS learning algorithm: covering through niching. The LCS must maintain a population of diverse rules that together solve a problem (e.g., classify examples). To maintain a diverse population while applying the GA's selection operator, the LCS must incorporate some kind of niching mechanism. The natural way to accomplish niching in an LCS is to force competing rules to share resources (i.e., rewards). This implicit LCS fitness sharing is similar to the explicit fitness sharing used in many niched GAs. Indeed, the LCS implicit sharing algorithm can be mapped onto explicit fitness sharing with a one-to-one correspondence between algorithm components. This mapping is important because several studies of explicit fitness sharing, and of niching in GAs generally, have produced key insights and analytical tools for understanding th...

We assume that the modality (i.e., number of local optima) of a fitness landscape is related to the difficulty of finding the best point on that landscape by evolutionary computation (e.g., hillclimbers and genetic algorithms (GAs)). We first examine the limits of modality by constructing a unimodal function and a maximally multimodal function. At such extremes our intuition breaks down. A fitness landscape consisting entirely of a single hill leading to the global optimum proves to be hard for hillclimbers but apparently easy for GAs. A provably maximally multimodal function, in which half the points in the search space are local optima, can be easy for both hillclimbers and GAs. Exploring the more realistic intermediate range between the extremes of modality, we construct local optima with varying degrees of "attraction" to our evolutionary algorithms. Most work on optima and their basins of attraction has focused on hills and hillclimbers, while some research has explored attraction...

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A new genetic algorithm for channel routing in the physical design process of VLSI circuits is presented. The algorithm is based on a problem specific representation The genetic encoding and our genetic operators are described in detail. The performance of the algorithm is tested on different benchmarks and it is shown that the results obtained using the proposed algorithm are either qualitatively similar to or better than the best published results.

This paper proposes genetic algorithms (GAs) for path planning and trajectory planning of an autonomous mobile robot. Our GA-based approach has an advantage of adaptivity such that the GAs work even if an environment is time-varying or unknown. Therefore, it is suitable for both off-line and on-line motion planning. We first presents a GA for path planning in a 2D terrain. Simulation results on the performance and adaptivity of the GA on randomly generated terrains are presented. Then, we discuss extensions of the GA for solving both path planning and trajectory planning simultaneously. 1 Introduction Motion planning [3] is one of the important tasks in intelligent control of an autonomous mobile robot. It is often decomposed into path planning and trajectory planning, although they are not independent of each other. Path planning is to generate a collision-free path in an environment with obstacles and optimize it with respect to some criterion. An algorithm for path planning is said...

In medicine and health care there are a lot of situations when patients have to be scheduled on different devices and/or to different physicians or therapists. May it concern preventive examinations, laboratory tests or convalescent therapies, we are always looking for an optimal schedule that would result in finishing all the activities scheduled as soon as possible, with the least patient waiting time and maximum device utilization. Since patient scheduling is highly complex problem, it is impossible to make a qualitative schedule by hand or even with exact heuristic methods. Therefore we developed a powerful automated scheduling method for highly constrained situations based on genetic algorithms and machine learning. In this paper we present the method, together with the whole process of schedule generation, the important parameters to direct the evolution and how the algorithm is guaranteed to produce only feasible solutions, not breaking any of the required constraints. We applied the described method to a problem of scheduling patients with different therapy needs to a limited number of therapeutic devices, but the algorithm can be easily modified to be used in similar situations. The results are quite encouraging and since all the solutions are feasible, the method can be easily incorporated into an interactive user interface, which can be of major importance when scheduling of patients, and human resources in general, is considered. 1.