AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
The Traveling Salesrep Problem, Edge Assembly Crossover, and 2-opt
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
An Ant Colony Optimization Approach to the Probabilistic Traveling Salesman Problem
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A genetic algorithm with a mixed region search for the asymmetric traveling salesman problem
Computers and Operations Research
New Operators of Genetic Algorithms for Traveling Salesman Problem
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Solving traveling salesman problems by combining global and local search mechanisms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A Bee Colony Optimization Algorithm for Traveling Salesman Problem
AMS '08 Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS)
Adaptive parameter control of evolutionary algorithms to improve quality-time trade-off
Applied Soft Computing
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
A novel genetic algorithm based on immunity
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A shadow price guided genetic algorithm for energy aware task scheduling on cloud computers
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
An evolutionary linear programming algorithm for solving the stock reduction problem
International Journal of Computer Applications in Technology
Hierarchical genetic-based grid scheduling with energy optimization
Cluster Computing
Security, energy, and performance-aware resource allocation mechanisms for computational grids
Future Generation Computer Systems
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The genetic algorithm (GA) is a popular global search algorithm. It has been used successfully in many fields, however, it is still challenging for the GA to obtain optimal solutions for complex problems. Another problem is that the GA can take a very long time to solve difficult problems. This paper proposes a new evolutionary algorithm that uses the fitness value to measure overall solutions and shadow prices to evaluate components. New shadow price guided operators are used to achieve good measurable evolutions. The new algorithm is used first to solve a simple optimization function and then applied to the complex traveling salesman problem (TSP). Simulation results have shown that the new shadow price guided evolutionary algorithm is effective in terms of performance and efficient in terms of speed.