By Adnan Acan (auth.), Jens Gottlieb, Günther R. Raidl (eds.)
This ebook constitutes the refereed court cases for the 4th eu convention on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2004, held in Coimbra, Portugal, in April including EuroGP 2004 and 6 workshops on evolutionary computing.
The 23 revised complete papers awarded have been rigorously reviewed and chosen from 86 submissions. one of the issues addressed are evolutionary algorithms in addition to metaheuristics like memetic algorithms, ant colony optimization, and scatter seek; the papers are facing representations, operators, seek areas, model, comparability of algorithms, hybridization of other tools, and idea. one of the combinatorial optimization difficulties studied are graph coloring, community layout, slicing, packing, scheduling, timetabling, touring salesman, car routing, and numerous different real-world purposes.
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Furthermore, the steady state evolutionary approach is replaced by a generational elitist EA. Preliminary experiments showed that these modifications gave better results and faster convergence (results not shown). Parameter settings: Window of adaptation (W): 100 individuals, Interval of adaptation (I): 20–50 evaluations, Shift percentage (S): 15%, Percentage of reward to pass back (P): 90%, Number of generations to pass back (M): 10. 2 The ADOPP Algorithm Julstrom’s Adaptive Operator Probabilities algorithm (ADOPP)  from 1995 is very similar to Davis’ approach.
First, we follow Levine and Ducatelle (2004) and group items according to their lengths. However, we then relate the lengths of an item to be packed to the filling degree of the current bin. Thus, we disregard the information about which items are already in the bin. Rather, the only thing that is important is how much space is left in the bin. 1 An entry in this matrix is thus denoted by where is the length of item and is the filling degree of a bin before item is to be filled in. By doing so we directly emphasize the importance to fill a bin as good as possible as opposed to emphasizing certain combinations of items that fill a bin as good as possible.
More precisely, a Hybrid Max-Min Ant System (denoted by HACO) is used. The solution construction mechanism used in this approach is based on the FFD heuristic and pheromone decoding is based on the pairwise favorability of item lengths in the same bin. The local search employed to improve the ants’ solutions is again based on the dominance result of Martello and Toth (1990). However, Levine and Ducatelle (2004) restrict the number of items to be replaced to two. The results show that the HACO approach finds comparable results to the HGGA.