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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 ..."
Abstract

Cited by 2 (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
CUSTOMIZED CROSSOVER IN EVOLUTIONARY SETS OF SAFE SHIP TRAJECTORIES
"... The paper presents selected aspects of evolutionary sets of safe ship trajectories—a method which applies evolutionary algorithms and some of the assumptions of game theory to solving ship encounter situations. For given positions and motion parameters of the ships, the method finds a near optimal s ..."
Abstract
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The paper presents selected aspects of evolutionary sets of safe ship trajectories—a method which applies evolutionary algorithms and some of the assumptions of game theory to solving ship encounter situations. For given positions and motion parameters of the ships, the method finds a near optimal set of safe trajectories of all ships involved in an encounter. The method works in real time and the solutions must be returned within one minute, which enforces speeding up the optimisation process. During the development of the method the authors tested various problemdedicated crossover operators to obtain the best performance. The results of that research are given here. The paper includes a detailed description of these operators as well as statistical simulation results and examples of experiment results.