Niching methods extend genetic algorithms to domains that require the location and maintenance of multiple solutions. Such domains include classification and machine learning, multimodal function optimization, multiobjective function optimization, and simulation of complex and adaptive systems. This study presents a comprehensive treatment of niching methods and the related topic of population diversity. Its purpose is to analyze existing niching methods and to design improved niching methods. To achieve this purpose, it first develops a general framework for the modelling of niching methods, and then applies this framework to construct models of individual niching methods, specifically crowding and sharing methods. Using a constructed model of crowding, this study determines why crowding methods over the last two decades have not made effective niching methods. A series of tests and design modifications results in the development of a highly effective form of crowding, called determin...

Genetic algorithms perform an adaptive search by maintaining a population of candidate solutions that are allocated dynamically to promising regions of the search space. The distributed nature of the genetic search provides a natural source of power for searching in changing environments. As long as sufficient diversity remains in the population the genetic algorithm can respond to a changing response surface by reallocating future trials. However, the tendency of genetic algorithms to converge rapidly reduces their ability to identify regions of the search space that might suddenly become more attractive as the environment changes. This paper presents a modification of the standard generational genetic algorithm that is designed to maintain the diversity required to track a changing response surface. An experimental study shows some promise for the new technique. 1. INTRODUCTION Genetic algorithms (GAs) have been shown to be a useful alternative to traditional search and optimization...

In this paper, we explore the use of alternative mutation strategies as a means of increasing diversity so that the GA can track the optimum of a changing environment. This paper contrasts three different strategies: the Standard GA using a constant level of mutation, a mechanism called Random Immigrants, that replaces part of the population each generation with randomly generated values, and an adaptive mechanism called Triggered Hypermutation, that increases the mutation rate whenever there is a degradation in the performance of the time-averaged best performance. The study examines each of these strategies in the context of several kinds of environmental change, including linear translation of the optimum, random movement of the optimum, and oscillation between two significantly different landscapes. These first results should lead to the development of a single mechanism that can work well in both stationary and nonstationary environments. 1

Recently, there has been increased interest in evolutionary computation applied to changing optimization problems. This paper surveys a number of approaches that extend the evolutionary algorithm with implicit or explicit memory, suggests a new benchmark problem and examines under which circumstances a memory may be helpful. From these observations we derive a new way to explore the benefits of a memory while minimizing its negative side effects.

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In this paper, we introduce a case-based method of initializing genetic algorithms that are used to guide search in changing environments. This is incorporated in an anytime learning system. Anytime learning is a general approach to continuous learning in a changing environment. The agent's learning module continuously tests new strategies against a simulation model of the task environment, and dynamically updates the knowledge base used by the agent on the basis of the results. The execution module includes a monitor that can dynamically modify the simulation model based on its observations of the external environment; an update to the simulation model causes the learning system to restart learning. Previous work has shown that genetic algorithms provide an appropriate search mechanism for anytime learning. This paper extends the approach by including strategies, which are learned under similar environmental conditions, in the initial population of the genetic algorithm. Experiments s...

If the optimization problem is dynamic, the goal is no longer to nd the extrema, but to track their progression through the space as closely as possible. This paper surveys a number of techniques that have been published in the literature in order to make evolutionary algorithms suitable for changing optimization problems.

Abstract—In recent years, interest in studying evolutionary algorithms (EAs) for dynamic optimization problems (DOPs) has grown due to its importance in real-world applications. Several approaches, such as the memory and multiple population schemes, have been developed for EAs to address dynamic problems. This paper investigates the application of the memory scheme for population-based incremental learning (PBIL) algorithms, a class of EAs, for DOPs. A PBIL-specific associative memory scheme, which stores best solutions as well as corresponding environmental information in the memory, is investigated to improve its adaptability in dynamic environments. In this paper, the interactions between the memory scheme and random immigrants, multipopulation, and restart schemes for PBILs in dynamic environments are investigated. In order to better test the performance of memory schemes for PBILs and other EAs in dynamic environments, this paper also proposes a dynamic environment generator that can systematically generate dynamic environments of different difficulty with respect to memory schemes. Using this generator, a series of dynamic environments are generated and experiments are carried out to compare the performance of investigated algorithms. The experimental results show that the proposed memory scheme is efficient for PBILs in dynamic environments and also indicate that different interactions exist between the memory scheme and random immigrants, multipopulation schemes for PBILs in different dynamic environments. Index Terms—Associative memory scheme, dynamic optimization problems (DOPs), immune system-based genetic algorithm (ISGA), memory-enhanced genetic algorithm, multipopulation scheme, population-based incremental learning (PBIL), random immigrants.

Fitness landscapes of real world problems are in general considered to be complex and often with both local and global peaks. In the static case the local peaks are interesting because they represent other potential solutions to the problem.

Abstract—We have developed a genetic algorithm (GA) approach to the problem of task scheduling for multiprocessor systems. Our approach requires minimal problem specific information and no problem specific operators or repair mechanisms. Key features of our system include a flexible, adaptive problem representation and an incremental fitness function. Comparison with traditional scheduling methods indicates that the GA is competitive in terms of solution quality if it has sufficient resources to perform its search. Studies in a nonstationary environment show the GA is able to automatically adapt to changing targets. Index Terms—Genetic algorithm, task scheduling, parallel processing. 1