M. Laumanns, G. Rudolph, and H.-P. Schwefel. A Spatial Predator-Prey Approach to Multi-Objective Optimization: A Preliminary Study. In T. Back, A. E. Eiben, M. Schoenauer, and H.-P. Schwefel, editors, PPSN V, pages 241--249. Springer, Berlin, 1998.  Home/Search   Document Details and Download   Summary   Related Articles   Check

.... any resemblance to natural computation [5] It is not known whether and how median selection is applicable to the more commonly used methods of self adaptation, like those in [12] 3] There exist also other evolutionary models, such as the predator prey approach to multi objective optimization [8], which use some form of continuous selection. The continuous selection presented in this work is motivated and developed on the basis of available ( #) ES theory [12] 3] The common motivations for using continuous selection are: The algorithm scales better in a multi processor ....

M. Laumanns, G. Rudolph, and H.-P. Schwefel. A spatial predatorprey approach to multi-objective optimization: A preliminary study. In Parallel Problem Solving from Nature -- PPSN V, pages 241--249, Berlin, 1998. Springer.

....fitness sharing is used to keep the diversity of the weight combinations and mating restrictions are required so that the algorithm can work properly. Most of the EMOAs are based on Genetic Algorithms and relatively little attention has been paid to evolution strategies. Some exceptions are [2, 7 9]. In [7] average ranking is used to dictate the deletion of a fraction of the population. A predator prey model is proposed in [9] A selection method that is similar to the VEGA approach [10] is adopted in [8] An algorithm called Pareto Archived Evolution Strategy (PAES) is suggested in [2] in ....

....can work properly. Most of the EMOAs are based on Genetic Algorithms and relatively little attention has been paid to evolution strategies. Some exceptions are [2, 7 9] In [7] average ranking is used to dictate the deletion of a fraction of the population. A predator prey model is proposed in [9]. A selection method that is similar to the VEGA approach [10] is adopted in [8] An algorithm called Pareto Archived Evolution Strategy (PAES) is suggested in [2] in which a non Pareto approach together with an archive of the found Pareto solutions are used. This paper investigates two methods ....

M. Laumanns, G. Rudolph, and H.-P. Schwefel. A spatial predator-prey approach to multi-objective optimization. In A.E. Eiben, Th. BSck, M. Schoenauer, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature, volume V, pages 241-249, 1998.

.... multiobjective search, such as convergence to the Pareto optimal front (Van Veldhuizen and Lamont, 1998a; Rudolph, 1998) niching (Obayashi et al. 1998) and elitism (Parks and Miller, 1998; Obayashi et al. 1998) while others have concentrated on developing new evolutionary techniques (Laumanns et al. 1998; Zitzler and Thiele, 1999) For a thorough discussion of evolutionary algorithms for multiobjective optimization, the interested reader is referred to Fonseca and Fleming (1995) Horn (1997) Van Veldhuizen and Lamont (1998b) and Coello (1999) In spite of this variety, there is a lack of ....

Laumanns, M., Rudolph, G. and Schwefel, H.-P. (1998). A spatial predator-prey approach to multiobjective optimization: A preliminary study. In Eiben, A. E., B ack, T., Schoenauer, M. and Schwefel, H.-P., editors, Fifth International Conference on Parallel Problem Solving from Nature (PPSN-V), pages 241--249, Springer, Berlin, Germany.

....necessity of multi objective optimization. Determining a functional context a multi objective optimization algorithm is required which does not result in a single realization but gives an appropriate coverage of the Pareto set. An algorithm complying these requirements is the predator prey model [7]. It is an evolutionary algorithm based on a structured population. Individuals (here: circuit parameter sets) correspond to the prey and are locally selected for replacement by different evaluation criteria corresponding to the predators. These perform a random walk and therefore drive the ....

M. Laumanns, G. Rudolph, and H.-P. Schwefel, "A Spatial Predator-Prey Approach to Multi-Objective Optimization," in Proc. Fifth Int'l Conf. Parallel Problem Solving From Nature (PPSN V) (T. Back, A. E. Eiben, M. Schoenauer, and H.-P. Schwefel, eds.), vol. 1498 of Lecture Notes in Computer Science, pp. 241--249, Springer, 1998.

.... successfully used these implementations in various multi objective optimization applications (Cunha et al. 1997; Eheart et al. 1993; Mitra et al. 1998; Parks and Miller, 1998; Weile et al. 1996) A number of studies have also concentrated on developing new GA implementations (Kursawe, 1990; Laumanns et al. 1998; Zitzler and Thiele, 1998) Fonseca and Fleming (1995) and Horn (1997) presented overviews of different multiobjective GA implementations, and Van Veldhuizen and Lamont (1998) made a survey of test problems that exist in the literature. Despite these interests, there seems to be a lack of ....

Laumanns, M., Rudolph, G. and Schwefel, H.-P. (1998). A spatial predator-prey approach to multi-objective optimization: A preliminary study. In Eiben, A. E., B ack, T., Schoenauer, M. and Schwefel, H.-P., editors, Parallel Problem Solving from Nature, V, pages 241--249, Springer, Berlin, Germany.

.... such as convergence to the Pareto optimal front (Veldhuizen and Lamont 1998a; Rudolph 1998) niching (Obayashi, Takahashi, and Takeguchi 1998) and elitism (Parks and Miller 1998; Obayashi, Takahashi, and Takeguchi 1998) while others have concentrated on developing new evolutionary techniques (Laumanns, Rudolph, and Schwefel 1998; Zitzler and Thiele 1999) For a thorough discussion of evolutionary algorithms for multiobjective optimization, the interested reader is referred to (Fonseca and Fleming 1995; Horn 1997; Veldhuizen and Lamont 1998b; Coello 1999) In spite of this variety, there is a lack of studies which ....

Laumanns, M., G. Rudolph, and H.-P. Schwefel (1998). A spatial predator-prey approach to multi-objective optimization: A preliminary study. In A. E. Eiben, T. Back, M. Schoenauer, and H.-P. Schwefel (Eds.), Fifth International Conference on Parallel Problem Solving from Nature (PPSN-V), Berlin, Germany, pp. 241-249. Springer.

....to more complex test problems and to real world engineering design problems. Till to date, most of the successful evolutionary implementations for multi criterion optimization rely on the concept of non domination. Although there are other concepts that can be used to develop a search algorithm [22], the concept of non domination is simple to use and understand. However, a recent study [4] has shown that search algorithms based on non domination need not always lead to the true Pareto optimal front. The algorithm can get stuck to a non dominated front which is different from the true ....

Laumanns, M., Rudolph, G., and Schwefel, H.-P. (1998). A spatial predator-prey approach to multiobjective optimization: A preliminary study. Proceedings of the Parallel Problem Solving from Nature, V. 241--249.

....function. Instead they are designed for using objective functions as a black box, thus are able to deal with complex simulation models, e.g. those discussed in Section 3.1. Apart from constraint handling techniques [2, Chapter C5] the capability to work on multi criteria optimization problems [19, 9, 18], and other fields, EAs have been used for solving mixed integer [22, 21, 33, 2] variable dimensional [32, 2] i.e. structure optimization problems. Besides other strategies two main EA approaches for structure optimization may be classified. The simultaneous structure evolution [31] basically ....

M. Laumanns, G. Rudolph, and H.-P. Schwefel. A Spatial Predator-Prey Approach to Multi-Objective Optimization. In Parallel Problem Solving From Nature --- PPSN V. Springer, Berlin and Heidelberg, 1998. To appear.

....to more complex test problems and to real world engineering design problems. Till to date, most of the successful evolutionary implementations for multi criterion optimization rely on the concept of non domination. Although there are other concepts that can be used to develop a search algorithm [28], the concept of non domination is simple to use and understand. However, a recent study [7] has shown that search algorithms based on non domination need not always lead to the true Pareto optimal front. The algorithm can get stuck to a non dominated front which is different from the true ....

Laumanns, M., Rudolph, G., and Schwefel, H.-P. (1998). A spatial predatorprey approach to multi-objective optimization: A preliminary study. Proceedings of the Parallel Problem Solving from Nature, V. 241--249.

.... 1997; Eheart, Cieniawski, and Ranjithan, 1993; Mitra, Deb, and Gupta, 1998; Parks and Miller, 1998; Weile, Michelsson, and Goldberg, 1996) A number of studies have also concentrated in developing new and improved GA implementations (Fonseca and Fleming, 1998; Leung et al. 1998; Kursawe, 1990; Laumanns, Rudolph, and Schwefel, 1998; Zitzler and Thiele, 1998a) Fonseca and Fleming (1995) and Horn (1997) have presented overviews of different multi objective GA implementations. Recently, van Veldhuizen and Lamont (1998) have made a survey of test problems that exist in the literature. Despite all these interests, there seems ....

Laumanns, M., Rudolph, G., and Schwefel, H.-P. (1998). A spatial predator-prey approach to multiobjective optimization: A preliminary study. Proceedings of the Parallel Problem Solving from Nature, V. 241--249.

.... Predefined schedules without feedback, Feedback based adaptation with (explicit) external control, and Self adaptive (internal) mechanisms without external control. Representatives of the first class are for instance time dependent schedules as applied in the Predator Prey EA of [LRS98] in a multi objective environment. Here, the mutation step sizes are discounted by a constant factor each time an offspring in produced. Convergence to the global Pareto set of some simple multi objective problems can be achieved, if the initial step sizes are big enough and a conservative ....

....the negative progress for larger step sizes seem to hinder an adequate preservation of step sizes around the optimal value. Of course, this assumption in only based on numerical experiment and lacks a theoretical validation still. 5 An Evolutionary Predator Prey Model The predator prey model of [LRS98] consists of a spatially distributed population. Predator individuals move across the spatial structure according to a random walk and perform selection: They delete the worst prey individual of their neighborhood according to their associated objective function. This is a population based single ....

Marco Laumanns, Gunter Rudolph, and Hans-Paul Schwefel. A spatial predator-prey approach to multiobjective optimization: A preliminary study. In Agoston E. Eiben et al., editor, Parallel Problem Solving from Nature (PPSN-V), pages 241--249, Berlin, 1998. Springer.

....Additionally, lots of constraint handling techniques exists [6, Chapt. C5] and even algorithmic variants that are able to handle variable dimensional problems [71,72] have been investigated. Finally, EAs have successfully shown their capability to work on multi criteria optimization problems [46,23,45]. Although EAs build a class of helpful solution techniques concerning realworld problems, some difficulties exist. In oder to ensure reliability and speed up the algorithms, the standard representations used in the canonical forms of EAs are no longer sufficient. Consequently, the standard ....

.... and diffusion models [30,76] it has been observed that local selection techniques not only yield a considerable speed up on parallel computers, but also improve the robustness of the algorithms [63,31] and ease the search for the whole Pareto optimal subset x ae S in case of multiple criteria [46]. Since a detailed presentation of parallel EAs are beyond the scope of this article, the reader is referred to [38] 13 3 NUCLEAR REACTOR CORE RELOAD DESIGN 3.1 Problem Description The following is a description of the generation of electricity in a nuclear power plant. The decay of atomic ....

M. Laumanns, G. Rudolph, and H.-P. Schwefel. A spatial predator-prey approach to multi-objective optimization: A preliminary study. In Eiben et al.

....A completely different, non generational, scheme of an EA has been devised recently for multi criteria as well as dynamic optimization. It is based on a predatorprey model and spatially distributed individuals, and moreover, it no longer needs any synchronization of birth and death processes [17]. The reader must be referred to the literature for further details. 4 ONE OLD AND SOME RECENT APPLICATIONS Just to demonstrate the versatility of an evolutionary approach to design and management challenges given from real world applications, a few examples will be mentioned, referenced, and ....

M. Laumanns, G. Rudolph, and H.-P. Schwefel, A spatial predator-prey approach to multi-objective optimization, In [42], pp. 241-249.

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M. Laumanns, G. Rudolph, and H.-P. Schwefel. A Spatial Predator-Prey Approach to Multi-Objective Optimization: A Preliminary Study. In T. Back, A. E. Eiben, M. Schoenauer, and H.-P. Schwefel, editors, PPSN V, pages 241--249. Springer, Berlin, 1998.

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Laumanns, M., Rudolph, G., and Schwefel, H.-P. (1998). A spatial predator-prey approach to multiobjective optimization: A preliminary study. Proceedings of the Parallel Problem Solving from Nature, V. 241--249.

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Springer. Laumanns, M., G. Rudolph, and H.-P. Schwefel (1998). A spatial predator-prey approach to multi-objective optimization: A preliminary study. In Fifth International Conference on Parallel Problem Solving from Nature (PPSN-V), pp. 241--249.

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Laumanns, M., Rudolph, G., and Schwefel, H.-P. (1998). A spatial predator-prey approach to multiobjective optimization: A preliminary study. Proceedings of the Parallel Problem Solving from Nature, V. 241--249.

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