Using evolutionary algorithms to generate a diverse set of solutions where all of them meet a given quality criteria has gained increasing interest in recent years. In order to gain theoretical insights on the working...
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ISBN:
(纸本)9781450334884
Using evolutionary algorithms to generate a diverse set of solutions where all of them meet a given quality criteria has gained increasing interest in recent years. In order to gain theoretical insights on the working principle of populationbased evolutionary algorithms for this kind of diversity optimization a first runtime analysis has been presented by Gao and Neumann [1] on the example problems OneMax and LeadingOnes. We continue this line of research and examine the diversity optimization process of population-based evolutionary algorithms on complete bipartite graphs for the classical vertex cover problem.
We present an evolutionary algorithm approach to schedule optimization for a group of production lines in a car factory. Schedules are evaluated with respect to the energy consumption over peak demand periods, while t...
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We present an evolutionary algorithm approach to schedule optimization for a group of production lines in a car factory. Schedules are evaluated with respect to the energy consumption over peak demand periods, while the task is to minimize the energy costs by appropriately scheduling the interruptions of processes on the lines. Tests on real problem instances show this approach gives near-optimal schedules in acceptable time.
In 1977 Makanin stated that the solvability problem for word equation systems is decidable ([10]). Makanin's algorithm is very complicated and the solvability problem for word equations remains NP-hard ([1]). We s...
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ISBN:
(纸本)3540222189
In 1977 Makanin stated that the solvability problem for word equation systems is decidable ([10]). Makanin's algorithm is very complicated and the solvability problem for word equations remains NP-hard ([1]). We show that testing solvability of word equation systems is a NP-complete problem if we look for solutions of length bounded by some given constant greater than or equal to two over some single letter alphabet. Up to this moment several evolutionary strategies have been proposed for other NP-complete problems, like 3-SAT, with a remarkable success. Following this direction we introduce here an evolutionary local search algorithm for solving word equation systems provided that some upper bound for the length of the solutions is given. We present some empirical results derived from our algorithm which indicate that our approach to this problem becomes a promising strategy. Our experimental results also certify that our local optimization technique clearly outperforms a simple genetic approach.
Network on Chip (NoC) is a new paradigm for design core based System on Chip (SoC) which supports high degree of reusability and provides increase computation power. This paper addresses the problem of topological map...
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This work deals with the problem of rostering employees at an airport. There are about a hundred different shifts in order to handle the irregular coverage constraints. Together, with the strict constraints, given by ...
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This work deals with the problem of rostering employees at an airport. There are about a hundred different shifts in order to handle the irregular coverage constraints. Together, with the strict constraints, given by the collective agreement, the problem becomes difficult to solve. Common one stage algorithms, applied to this problem, produce rosters containing too many isolated days-on and days-off which makes the roster unusable. This paper suggests a three stage approach for the employees rostering problem where a set of different shifts is needed to satisfy the coverage requirements. The solution is based on the problem transformation to a simpler problem, thereupon, an evolutionary algorithm is used to determine a rough position of the shifts in the roster. The maximal weighted matching in the bipartite graph is used as the inverse transformation of the problem and the final roster is obtained by the optimization based on a Tabu Search algorithm.
This paper presents a new evolutionary algorithm based on the principle of echolocation, also called biosonar. This principle is active in numerous animals such as shrews and bats. These animals use it as radar in ord...
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ISBN:
(纸本)9780983762409
This paper presents a new evolutionary algorithm based on the principle of echolocation, also called biosonar. This principle is active in numerous animals such as shrews and bats. These animals use it as radar in order to find food, obstacles or locate objects. The algorithm presented mimics this echolocation principle to explore the search space and obtain a diverse number of solutions for a combinatorial optimization problem. The well-known multi-objective redundancy allocation problem is used to show the performance of this new evolutionary approach. The algorithm considers the maximization of system reliability, the minimization of system cost, and the minimization of system weight to be optimized simultaneously. The solution to the multi-objective optimization problem is a set of Pareto-optimal solutions.
In case of restricting the relation between the parents and the offspring as each one in order to learn in real time, a new numerical formula is proposed to solve the difficulty of adjusting the search region as reduc...
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In quantum information theory, entanglement distillation is a key component for designing quantum computer networks and quantum repeaters. In this work, the practical entanglement distillation problem is re-designed i...
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In the present paper, the performances of surrogate-assisted evolutionary algorithms for dynamic identification problems and damage detection are investigated. An improved algorithm is designed to limit the computatio...
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High-dimensional data often threatens the performance of classification algorithms. We propose a two-step approach for dealing with high-dimensional data. In the first step, features are arranged into bins, where each...
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ISBN:
(纸本)9781450311786
High-dimensional data often threatens the performance of classification algorithms. We propose a two-step approach for dealing with high-dimensional data. In the first step, features are arranged into bins, where each bin corresponds to a much smaller sub-space of features. In the second step, classifiers are independently applied to the set of features within each sub-space, and their results are then aggregated. We consider slicing a space Rd into smaller sub-spaces as a multi-objective search problem, which can be solved by evolutionary algorithms. We performed a systematic evaluation using three classification algorithms on high-dimensional data. Copyright is held by the author/owner(s).
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