In this paper, we propose a differential evolution algorithm to solve constrained optimization problems. Our approach uses three simple selection criteria based on feasibility to guide the search to the feasible region. The proposed approach does not require any extra parameters other than those normally adopted by the Differential Evolution algorithm. The present approach was validated using t...

A novel application to the optimization of neural networks is presented in this paper. Here, the weight and architecture optimization of neural networks can be formulated as a mixed-integer optimization problem. And then a mixed-integer evolutionary algorithm (Mixed-Integer Hybrid Differential Evolution, MIHDE) is used to optimize the neural network. Finally, the optimized neural network is app...

Classifying and Counting Vehicles in Traffic Control Applications Francesco Archetti, Enza Messina, Daniele Toscani, Leonardo Vanneschi A Neural Evolutionary Classification Method for Brain-Wave Analysis Antonia Azzini, Andrea G.B. Tettamanzi Differential Evolution applied to a multimodal information theoretic optimization problem Patricia Besson, Jean-Marc Vesin, Vlad Popovici, Murat Kunt Arti...

Differential Evolution (DE) was very successful in solving the global continuous optimization problem. It mainly uses the distance and direction information from the current population to guide its further search. Estimation of Distribution Algorithm (EDA) samples new solutions from a probability model which characterizes the distribution of promising solutions. This paper proposes a combinatio...

Abstract In this paper we present experimental results to show deep view on how selfadaptive mechanism works in differential evolution algorithm. The results of the self-adaptive differential evolution algorithm were evaluated on the set of 24 benchmark functions provided for the CEC2006 special session on constrained real parameter optimization. In this paper we especially focus on how the con...

Nowadays, dust phenomenon is one of the important challenges in warm and dry areas. Forecasting the phenomenon before its occurrence helps to take precautionary steps to prevent its consequences. Fuzzy expert systems capabilities have been taken into account to assist and cope with the uncertainty associated to complex environments such as dust forecasting problem. This paper presents novel hyb...

This paper proposes a novel surrogate-model-based multiobjective evolutionary algorithm called Differential Evolution for Multiobjective Optimization Based on Gaussian Process Models (GP-DEMO). The algorithm is based on the newly defined relations for comparing solutions under uncertainty. These relations minimize the possibility of wrongly performed comparisons of solutions due to inaccurate s...

despite the growing use of evolutionary multi-objective optimization algorithms in different categories of science, these algorithms as a powerful tool in portfolio optimization and specially solving multi-objective portfolio optimization problem is still in its early stages. in this paper, moeas have been used for solving multi-objective portfolio optimization problem in tehran stock market. f...