Matrix multiplication via arithmetic progressions
Journal of Symbolic Computation - Special issue on computational algebraic complexity
Relations and graphs: discrete mathematics for computer scientists
Relations and graphs: discrete mathematics for computer scientists
Branching programs and binary decision diagrams: theory and applications
Branching programs and binary decision diagrams: theory and applications
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Introduction to Algorithms
Implementation of Relational Algebra Using Binary Decision Diagrams
ReIMICS '01 Revised Papers from the 6th International Conference and 1st Workshop of COST Action 274 TARSKI on Relational Methods in Computer Science
RANDOM '98 Proceedings of the Second International Workshop on Randomization and Approximation Techniques in Computer Science
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
Efficient Relational Calculation for Software Analysis
IEEE Transactions on Software Engineering
Relational implementation of simple parallel evolutionary algorithms
RelMiCS'05 Proceedings of the 8th international conference on Relational Methods in Computer Science, Proceedings of the 3rd international conference on Applications of Kleene Algebra
RelView: an OBDD-based computer algebra system for relations
CASC'05 Proceedings of the 8th international conference on Computer Algebra in Scientific Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Edge sets: an effective evolutionary coding of spanning trees
IEEE Transactions on Evolutionary Computation
Analyses of simple hybrid algorithms for the vertex cover problem*
Evolutionary Computation
Evaluating sets of search points using relational algebra
RelMiCS'06/AKA'06 Proceedings of the 9th international conference on Relational Methods in Computer Science, and 4th international conference on Applications of Kleene Algebra
Hi-index | 0.00 |
We take a relation-algebraic view on the formulation of evolutionary algorithms in discrete search spaces. First, we show how individuals and populations can be represented as relations and formulate some standard mutation and crossover operators for this representation using relation-algebra. Evaluating a population with respect to their constraints seems to be the most costly step in one generation for many important problems. We show that the evaluation process for a given population can be sped up by using relation-algebraic expressions in the process. This is done by examining the evaluation of possible solutions for three of the best-known NP-hard combinatorial optimization problems on graphs, namely the vertex cover problem, the computation of maximum cliques, and the determination of a maximum independent set. Extending the evaluation process for a given population to the evaluation of the whole search space we get exact methods for the considered problems, which allow to evaluate the quality of solutions obtained by evolutionary algorithms.