By Thomas Gaube, Franz Rothlauf (auth.), Egbert J. W. Boers (eds.)
This booklet constitutes the refereed complaints of 5 application-oriented workshops held at the same time as EvoWorkshops 2001 in Como, Italy in April 2001.
The fifty two revised complete papers provided have been rigorously reviewed and chosen out of seventy five submissions. The papers are geared up in topical sections on graph difficulties, Knapsack difficulties, ant algorithms, task difficulties, evolutionary algorithms research, permutative difficulties, aeronautics, photo research and sign processing, evolutionary studying, and evolutionary scheduling and timetabling.
Read or Download Applications of Evolutionary Computing: EvoWorkshops 2001: EvoCOP, EvoFlight, EvoIASP, EvoLearn, and EvoSTIM Como, Italy, April 18–20, 2001 Proceedings PDF
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Extra resources for Applications of Evolutionary Computing: EvoWorkshops 2001: EvoCOP, EvoFlight, EvoIASP, EvoLearn, and EvoSTIM Como, Italy, April 18–20, 2001 Proceedings
A number of approaches based on GRASP are presented for the Multiconstraint Knapsack Problem. GRASP combines greedy construction of feasible solutions with local search. Results from applying our algorithms to standard test problems are presented and compared with results obtained by Chu and Beasley. 1 Introduction In this paper, we consider the application of GRASP (Greedy Randomized Adaptive Search Procedure) to the multiconstraint knapsack problem (MKP). The paper is structured as follows. In the current section, we begin by giving a brief introduction to GRASP; we then introduce some notation for describing the MKP and briefly review other metaheuristic approaches for the MKP.
A Branch and Bound Method for the Multiconstraint Zero-One Knapsack Problem. J. of the Operational Res. Soc. 30 (1979) 369–378 4. , Troya, J. : A hybrid genetic algorithm for the 0-1 multiple knapsack problem. In: Artificial neural nets and genetic algorithms 3, eds. Smith, G. , Steele, N. , Albrecht, R. , Springer-Verlag (1998) 251–255 5. Chu, P. , Beasley, J. : A genetic algorithm for the multidimensional knapsack problem. J. of Heuristics 4 (1998) 63–86 6. : A Heuristic Search Procedure for the Multiconstraint Zero-One Knapsack Problem.
By doing this, the objective in a max-instance is transformed to max pe−py. Since pe is a constant, this is equivalent to minimizing py. Thus, comp((max, N, M, p, R, b)) = (min, N, M, p, R, Re − b). Similarly, comp((min, N, M, p, R, b)) = (max, N, M, p, R, Re − b). A number of researchers have applied metaheuristic approaches to the MKP (see, for example, [4,5]). Chu and Beasley’s algorithm  appears to be the most successful GA to date for the MKP. In their algorithm, infeasible solutions are “repaired” using a greedy heuristic based on Pirkul’s surrogate duality approach .
Applications of Evolutionary Computing: EvoWorkshops 2001: EvoCOP, EvoFlight, EvoIASP, EvoLearn, and EvoSTIM Como, Italy, April 18–20, 2001 Proceedings by Thomas Gaube, Franz Rothlauf (auth.), Egbert J. W. Boers (eds.)