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Imperialist Competitive Algorithm (ICA) is a new socio-politically motivated global search strategy that has recently been introduced for dealing with different optimization problems. In this paper, we adopt ICA to solve the quadratic assignment problem which is a NP-Complete problem and is one of the most interesting and most challenging combinatorial optimization problems in existence. We test our...
Reinforcement Programming (RP) is a new approach to automatically generating algorithms, that uses reinforcement learning techniques. This paper describes the RP approach and gives results of experiments using RP to generate a generalized, in-place, iterative sort algorithm. The RP approach improves on earlier results that that use genetic programming (GP). The resulting algorithm is a novel algorithm...
A coevolutionary algorithm as a search strategy adaptation procedure in constrained optimization is discussed in the paper. The coevolutionary algorithm consists of the set of individual conventional genetic algorithms with different search strategies. Individual genetic algorithms compete and cooperate with each other. Competition is provided with resource re-allocation among algorithms and cooperation...
This paper aims to compare the effect of dynamically controlling the exploration-exploitation trade-off in cellular Genetic Algorithms (cGAs) from two perspectives: first, through lattice reconfiguration while dynamically changing the grid-neighbourhood ratio and thus taking advantage of their inherent structural properties; second, through local selection using a recently developed method known as...
The shortcomings of existing intelligent optimization algorithms are easy to produce premature convergence, easy to fall into local optimal equilibrium states, and poor efficiency at evolutionary late stage. In order to overcome the above shortcomings, a variety of new strategies and approaches were put forward by researchers in various countries. Although the orthogonal design has been applied to...
A modified dynamic differential evolution was proposed for discrete optimization. Based on the new framework of dynamic differential evolution, two additional operators were used to extend the dynamic differential evolution to the field of discrete optimization. The first operator was the mapping operator, which could map the continuous value into zero or one. The other new operator was the boundary...
In this paper we explore using genetic algorithms to construct universal hash functions to efficiently hash a given set of keys. The hash function generated in this way should give minimum number of collisions. The algorithm has less computational complexity and can be used in scenarios where the input distribution of keys is changing and the hash function needs to be modified often to rehash the...
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