Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Application of Evolution Strategy in Parallel Populations
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
An Evolutionary Algorithm for Integer Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Metric Based Evolutionary Algorithms
Proceedings of the European Conference on Genetic Programming
Selected Papers from AISB Workshop on Evolutionary Computing
On the design of problem-specific evolutionary algorithms
Advances in evolutionary computing
From Syntactical to Semantical Mutation Operators for Structure Optimization
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Evolutionary computing in manufacturing industry: an overview of recent applications
Applied Soft Computing
Mixed-Integer evolution strategies and their application to intravascular ultrasound image analysis
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Problem-specific search operators for metaheuristic software architecture design
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
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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 to calculate target function values.To represent chemical engineering plants, a network representation with typed vertices and variable structure will be introduced. For this representation, we introduce a technique on how to create problem specific search operators and apply them in stochastic optimization procedures. The applicability of the approach is demonstrated by a reference example.The design of the algorithms will be oriented at the systematic framework of metric-based evolutionary algorithms (MBEAs). MBEAs are a special class of evolutionary algorithms, fulfilling certain guidelines for the design of search operators, whose benefits have been proven in theory and practice. MBEAs rely upon a suitable definition of a metric on the search space. The definition of a metric for the graph representation will be one of the main issues discussed in this paper.Although this article deals with the problem domain of chemical plant optimization, the algorithmic design can be easily transferred to similar network optimization problems. A useful distance measure for variable dimensionality search spaces is suggested.