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A Survey of Evolution Strategies
 Proceedings of the Fourth International Conference on Genetic Algorithms
, 1991
"... Similar to Genetic Algorithms, Evolution Strategies (ESs) are algorithms which imitate the principles of natural evolution as a method to solve parameter optimization problems. The development of Evolution Strategies from the first mutationselection scheme to the refined (¯,)ES including the gen ..."
Abstract

Cited by 224 (3 self)
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Similar to Genetic Algorithms, Evolution Strategies (ESs) are algorithms which imitate the principles of natural evolution as a method to solve parameter optimization problems. The development of Evolution Strategies from the first mutationselection scheme to the refined (¯,)ES including the general concept of selfadaptation of the strategy parameters for the mutation variances as well as their covariances are described. 1 Introduction The idea to use principles of organic evolution processes as rules for optimum seeking procedures emerged independently on both sides of the Atlantic ocean more than two decades ago. Both approaches rely upon imitating the collective learning paradigm of natural populations, based upon Darwin's observations and the modern synthetic theory of evolution. In the USA Holland introduced Genetic Algorithms in the 60ies, embedded into the general framework of adaptation [Hol75]. He also mentioned the applicability to parameter optimization which was fir...
Parallel Approaches to Stochastic Global Optimization
 In Parallel Computing: From Theory to Sound Practice, W. Joosen and E. Milgrom, Eds., IOS
, 1992
"... In this paper we review parallel implementations of some stochastic global optimization methods on MIMD computers. Moreover, we present a new parallel version of an Evolutionary Algorithm for global optimization, where the inherent parallelism can be scaled to obtain a reasonable processor utilizati ..."
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Cited by 13 (5 self)
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In this paper we review parallel implementations of some stochastic global optimization methods on MIMD computers. Moreover, we present a new parallel version of an Evolutionary Algorithm for global optimization, where the inherent parallelism can be scaled to obtain a reasonable processor utilization. For this algorithm the convergence to the global optimum with probability one can be assured. Test results concerning speed up and reliability are given. 1 Introduction Many real world problems in engineering and economics can be formulated as optimization problems, in which the objective function is multimodal, i.e. the problem possesses many local minima. Compared to the number of methods designed to determine a local minimum, there are only a few methods which attempt to find the global minimum (see [52] for a survey). Although there are some special cases where the global optimum can be found (see [26]) the general case is unsolvable. This paper will be restricted to the more gener...
Optimization of chemical engineering process structures by means of Evolutionary Algorithms
"... This paper describes the adaptation of Evolutionary Algorithms (EAs) to the structural optimization of chemical engineering plants, using rigorous process simulation combined with realistic costing procedures as target function. The main issue for this adaptation relies on the design of a suitable r ..."
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This paper describes the adaptation of Evolutionary Algorithms (EAs) to the structural optimization of chemical engineering plants, using rigorous process simulation combined with realistic costing procedures as target function. The main issue for this adaptation relies on the design of a suitable representation of chemical processes as special kinds of parameterized graphs together with corresponding genetic operators. A general catalogue of design guidelines for EA is presented. These guidelines are taken under consideration, when designing the problem specific graph representation and genetic operators. Hereby, the formulation of distance measures on discrete search spaces turns out to be important. Finally, some test runs demonstrate the applicability of the EA, thereby underpinning the usefulness of the design principles.
sensitivity and perturbation analysis
, 2009
"... Inferring Drosophila gap gene regulatory network: a parameter ..."
unknown title
, 2007
"... Efficient parameter estimation for spatiotemporal models of pattern formation: Case study of Drosophila melanogaster ..."
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Efficient parameter estimation for spatiotemporal models of pattern formation: Case study of Drosophila melanogaster
A Comparison of Parallel Global Optimisation Algorithms for Reverse Engineering Gene Networks
, 2008
"... The approach of reverse engineering gene networks by fitting models has been highly successful, but increasing model complexity means that more powerful global optimisation techniques are required for model fitting; for this, we require faster parallel algorithms. I examine the modeling of the gap g ..."
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The approach of reverse engineering gene networks by fitting models has been highly successful, but increasing model complexity means that more powerful global optimisation techniques are required for model fitting; for this, we require faster parallel algorithms. I examine the modeling of the gap gene network in Drosophila, for which a gene network model, in the form of a set of differential equations, is fitted to highresolution spatiotemporal expression data. Previously model fitting has been performed with Parallel Lam Simulated Annealing, but it has been shown that an island Evolutionary Strategy would be more efficient. Until now a parallel Evolutionary Strategy has not been applied to this problem. I study in detail the performance of the island Evolutionary Strategy when applied to the gap gene problem, including the effect of the distribution of individuals across islands, and demonstrate that the perisland speed of the algorithm increases with number of islands. By splitting up the islands throughout a number of processors, I apply a new coarsegrained parallel Evolutionary Strategy to problem, and study how its performance varies depending on number of nodes. It is found that both the reliability and the speed of the algorithm increase