J. Sun, Q. Zhang and E. Tsang, "DE/DEA: New Evolutionary Algorithm for Global Optimisation", Information Sciences (169), 2005, pp249-262.
In this paper, we combined EDA and DE together.
Estimation of Distribution Algorithm+ Two Local Search Methods
Q. Zhang, J. Sun, E. Tsang and J. Ford, "Hybrid Estimation of Distribution Algorithm for Global Optimsation", Engineering Computations, vol. 21, no. 1, 2004, pp91-107.
This paper introduces a new hybrid evolutionary algorithm for continuous global optimisation problems, called Estimation of Distribution algorithm with Local search (EDA/L). Like other evolutionary algorithms, EDA/L maintains and improves a population of solutions in the feasible region. Initial candidate solutions are generated by uniform design, these solutions evenly scatter over the feasible solution region. To generate a new population, a marginal histogram model is built based on the global statistical information extracted from the current population, and then new solutions are sampled from the model thus built. The incomplete simplex method applies to every new solution generated by uniform design or sampled from the histogram model. UOBDQA (unconstrained optimisation by diagonal quadratic approximation) applies to several selected resultant solutions of the incomplete simplex method at each generation. We study the effectiveness of main components of EDA/L. The experimental results demonstrate that EDA/L is better than four other recent evolutionary algorithms in terms of the solution quality and the computational cost.
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last updated March, 2006, Q. Zhang