Titlebook: Evolutionary Computation in Combinatorial Optimization; 23rd European Confer Leslie Pérez Cáceres,Thomas Stützle Conference proceedings 202

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,A Policy-Based Learning Beam Search for?Combinatorial Optimization,e only theoretically analyzed, are considered and evaluated in practice on the well-studied Longest Common Subsequence (LCS) problem. To keep P-LBS scalable to larger problem instances, a bootstrapping approach is further proposed for training. Results on established sets of LCS benchmark instances

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,The Cost of?Randomness in?Evolutionary Algorithms: Crossover can Save Random Bits,hms, that the total cost of randomness during all crossover operations on . is only .. Consequently, the use of crossover can reduce the cost of randomness below that of the fastest evolutionary algorithms that only use standard mutations.

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,Multi-objectivization Relaxes Multi-funnel Structures in?Single-objective NK-landscapes, global optimum of an artificially generated helper problem via the Pareto local optimal solutions. Experimental results showed that the proposed MOLS achieved a higher success rate of the target single-objective optimization than iterative local search algorithms on target .-landscape problems with

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Recessive

https://doi.org/10.1057/978-1-137-41465-6do in OR-Tools?[.], where we achieve significant cost savings, faster runtime, and memory savings by order of magnitude. Performance on large-scale real-world instances with more than 300 vehicles and 1,200 pickup and delivery requests is also presented, achieving less than an hour runtimes.

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清澈

CULP

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The Future of Large-Scale Migration, global optimum of an artificially generated helper problem via the Pareto local optimal solutions. Experimental results showed that the proposed MOLS achieved a higher success rate of the target single-objective optimization than iterative local search algorithms on target .-landscape problems with

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Anthropologies and Their Relationshipsnteger linear program (MILP) in a direct way as well as solving the instances with a construction heuristic (CH). Results show that MLO scales substantially better for such large instances than the MILP or the CH.