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A C++ APPLICATION PROGRAMMING INTERFACE FOR BIASED RANDOMKEY GENETIC ALGORITHMS
"... Abstract. In this paper, we describe brkgaAPI, an efficient and easytouse object oriented application programming interface for the algorithmic framework of biased randomkey genetic algorithms. Our crossplatform library automatically handles the large portion of problemindependent modules that ..."
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Cited by 9 (8 self)
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Abstract. In this paper, we describe brkgaAPI, an efficient and easytouse object oriented application programming interface for the algorithmic framework of biased randomkey genetic algorithms. Our crossplatform library automatically handles the large portion of problemindependent modules that are part of the framework, including population management and evolutionary dynamics, leaving to the user the task of implementing a problemdependent procedure to convert a vector of random keys into a solution to the underlying optimization problem. Our implementation is written in the C++ programming language and may benefit from sharedmemory parallelism when available. 1.
A biased randomkey genetic algorithm for road congestion minimization
, 2010
"... One of the main goals in transportation planning is to achieve solutions for two classical problems, the traffic assignment and toll pricing problems. The traffic assignment problem aims to minimize total travel delay among all travelers. Based on data derived from the first problem, the toll prici ..."
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Cited by 9 (3 self)
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One of the main goals in transportation planning is to achieve solutions for two classical problems, the traffic assignment and toll pricing problems. The traffic assignment problem aims to minimize total travel delay among all travelers. Based on data derived from the first problem, the toll pricing problem determines the set of tolls and corresponding tariffs that would collectively benefit all travelers and would lead to a user equilibrium solution. Obtaining highquality solutions for this framework is a challenge for large networks. In this paper, we propose an approach to solve the two problems jointly, making use of a biased randomkey genetic algorithm for the optimization of transportation network performance by strategically allocating tolls on some of the links of the road network. Since a transportation network may have thousands of intersections and hundreds of road segments, our algorithm takes advantage of mechanisms for speeding up shortestpath algorithms.
A Biased RandomKey Genetic Algorithm with ForwardBackward Improvement for the Resource Constrained Project Scheduling Problem
"... This paper presents a biased randomkeys genetic algorithm for the Resource Constrained Project Scheduling Problem. The chromosome representation of the problem is based on random keys. Active schedules are constructed using a priorityrule heuristic in which the priorities of the activities are de ..."
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Cited by 6 (4 self)
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This paper presents a biased randomkeys genetic algorithm for the Resource Constrained Project Scheduling Problem. The chromosome representation of the problem is based on random keys. Active schedules are constructed using a priorityrule heuristic in which the priorities of the activities are de ned by the genetic algorithm. A forwardbackward improvement procedure is applied to all solutions. The chromosomes supplied by the genetic algorithm are adjusted to re ect the solutions obtained by the improvement procedure. The heuristic is tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the e ectiveness of the proposed algorithm.
Survivable IP/MPLSOverWSON multilayer network optimization
 IEEE/OSA Journal of Optical Communications and Networking (JOCN
, 2011
"... Abstract—Network operators are facing the problem of dimensioning their networks for the expected huge IP traffic volumes while keeping constant or even reducing the connectivity prices. Therefore, new architectural solutions able to cope with the expected traffic increase in a more costeffective w ..."
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Cited by 3 (2 self)
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Abstract—Network operators are facing the problem of dimensioning their networks for the expected huge IP traffic volumes while keeping constant or even reducing the connectivity prices. Therefore, new architectural solutions able to cope with the expected traffic increase in a more costeffective way are needed. In this work, we study the survivable IP/multiprotocol label switching (MPLS) over wavelength switched optical network (WSON) multilayer network problem as a capital expenditure (CAPEX) minimization problem. Two network approaches providing survivability against optical links, IP/MPLS nodes, and optoelectronic port failures are compared: the classical overlay approach where two redundant IP/MPLS networks are deployed, and the new joint multilayer approach which provides the requested survivability through an orchestrated interlayer recovery scheme which minimizes the overdimensioning of IP/MPLS nodes. Mathematical programming models are developed for both approaches. Solving these models, however, becomes impractical for realistic networks. In view of this, evolutionary heuristics based on the biased randomkey genetic algorithm framework are also proposed. Exhaustive experiments on several reference network scenarios illustrate the effectiveness of the proposed approach in minimizing network CAPEX. Index Terms—Integer linear programming; Multilayer planning; Survivable multilayer networks. I.
Randomkey genetic algorithms
, 2014
"... A randomkey genetic algorithm is an evolutionary metaheuristic for discrete and global optimization. Each solution is encoded as a vector of n random keys, where a random key is a real number, randomly generated, in the continuous interval [0, 1). A decoder maps each vector of random keys to a solu ..."
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A randomkey genetic algorithm is an evolutionary metaheuristic for discrete and global optimization. Each solution is encoded as a vector of n random keys, where a random key is a real number, randomly generated, in the continuous interval [0, 1). A decoder maps each vector of random keys to a solution of the optimization problem being solved and computes its cost. The algorithm starts with a population of p vectors of random keys. At each iteration, the vectors are partitioned into two sets, a smaller set of highvalued elite solutions, and the remaining nonelite solutions. All elite elements are copied, without change, to the next population. A small number of randomkey vectors (the mutants) is added to the population of the next iteration. The remaining elements of the population of the next iteration are generated by combining, with the parametrized uniform crossover of Spears and DeJong [58], pairs of solutions. This chapter reviews randomkey genetic algorithms and describes an effective variant called biased randomkey genetic algorithms.
Contents lists available at SciVerse ScienceDirect European Journal of Operational Research
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