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EdgeSets: An Effective Evolutionary Coding of Spanning Trees
, 2002
"... The fundamental design choices in an evolutionary algorithm are its representation of candidate solutions and the operators that will act on that representation. We propose representing spanning trees in evolutionary algorithms for network design problems directly as sets of their edges, and we d ..."
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The fundamental design choices in an evolutionary algorithm are its representation of candidate solutions and the operators that will act on that representation. We propose representing spanning trees in evolutionary algorithms for network design problems directly as sets of their edges, and we describe initialization, recombination, and mutation operators for this representation. The operators offer
Biased mutation operators for subgraphselection problems
 IN IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 2004
"... Many graph problems seek subgraphs of minimum weight that satisfy a set of constraints. Examples include the minimum spanning tree problem (MSTP), the degreeconstrained minimum spanning tree problem (dMSTP), and the traveling salesman problem (TSP). Lowweight edges predominate in optimum solution ..."
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Cited by 4 (0 self)
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Many graph problems seek subgraphs of minimum weight that satisfy a set of constraints. Examples include the minimum spanning tree problem (MSTP), the degreeconstrained minimum spanning tree problem (dMSTP), and the traveling salesman problem (TSP). Lowweight edges predominate in optimum solutions to such problems, and the performance of evolutionary algorithms (EAs) is often improved by biasing variation operators to favor these edges. We investigate the impact of biased edgeexchange mutation. In a largescale empirical investigation, we study the distributions of edges in optimum solutions of the MSTP, the dMSTP, and the TSP in terms of the edges ’ weightbased ranks. We approximate these distributions by exponential functions and derive approximately optimal probabilities for selecting edges to be incorporated into candidate solutions during mutation. A theoretical analysis of the expected running time
On WeightBiased Mutation for Graph Problems
"... Many graph problems seek subgraphs of minimum weight that satisfy the problems' constraints. Examples include the degreeconstrained minimum spanning tree and traveling salesman problems. Lowweight edges ..."
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Many graph problems seek subgraphs of minimum weight that satisfy the problems' constraints. Examples include the degreeconstrained minimum spanning tree and traveling salesman problems. Lowweight edges
Dynamic Power Minimization During Combinational Circuit Testing as a Traveling Salesman Problem
"... Testing of VLSI circuits can cause generation of excessive heat which can damage the chips under test. In the random testing environment, highperformance CMOS circuits consume significant dynamic power during testing because of enhanced switching activity in the internal nodes. Our work focuses on ..."
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Cited by 2 (0 self)
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Testing of VLSI circuits can cause generation of excessive heat which can damage the chips under test. In the random testing environment, highperformance CMOS circuits consume significant dynamic power during testing because of enhanced switching activity in the internal nodes. Our work focuses on the fact that power minimization is a Traveling Salesman Problem (TSP). We explore application of local search and genetic algorithms to test set reordering and perform a quantitative comparison to previously used deterministic techniques. We also consider reduction of the original test set as a dualobjective optimization problem, where switching activity and fault coverage are the two objective functions.
Making the EdgeSet Encoding Fly by Controlling the Bias of its Crossover Operator
, 2004
"... The edgeset encoding is a direct tree encoding which applies search operators directly to trees represented as sets of edges. There are two variants of crossover operators for the edgeset encoding: With heuristics that consider the weights of the edges, or without heuristics. Due to a strong bia ..."
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The edgeset encoding is a direct tree encoding which applies search operators directly to trees represented as sets of edges. There are two variants of crossover operators for the edgeset encoding: With heuristics that consider the weights of the edges, or without heuristics. Due to a strong bias of the heuristic crossover operator towards the minimum spanning tree (MST) a population of solutions converges quickly towards the MST and EAs using this operator show low performance when used for tree optimization problems where the optimal solution is not the MST. This paper presents a modied crossover operator (
TX) that allows us to control the bias towards the MST. The bias can be set arbitrarily between the strong bias of the heuristic crossover operator, or being unbiased. An investigation into the performance of EAs using the TX for randomly created OCST problems of dierent types and OCST test instances from the literature present good results when setting the crossoverspecic parameter properly. The presented results suggest that the original heuristic crossover operator of the edgesets should be substituted by the modiedTX operator that allows us to control the bias towards the MST. 1
AntBased Crossover for Permutation Problems
"... Abstract. Crossover for evolutionary algorithms applied to permutation problems is a difficult and widely discussed topic. In this paper we use ideas from ant colony optimization to design a new permutation crossover operator. One of the advantages of the new crossover operator is the ease to introd ..."
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Abstract. Crossover for evolutionary algorithms applied to permutation problems is a difficult and widely discussed topic. In this paper we use ideas from ant colony optimization to design a new permutation crossover operator. One of the advantages of the new crossover operator is the ease to introduce problem specific heuristic knowledge. Empirical tests on a travelling salesperson problem show that the new crossover operator yields excellent results and significantly outperforms evolutionary algorithms with edge recombination operator as well as pure ant colony optimization. 1
1.3 Theoretical Foundations and Practical Design of EAs............. 5
, 2002
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