Protein structure optimisation is a well-known problem in bioinformatics. This work applies an evolutionary algorithm to solve the protein structure optimisation problem based on the AB off-lattice model. Three different implementations of the differential evolution (DE) algorithm were developed, a sequential and two parallel. The parallel implementations (master-slave and ring-island) showed s...

For many soft computing methods, we need to generate random numbers to use either as initial estimates or during the learning and search process. Recently, results for evolutionary algorithms, reinforcement learning and neural networks have been reported which indicate that the simultaneous consideration of randomness and opposition is more advantageous than pure randomness. This new scheme, ca...

Differential Evolution (DE) is arguably one of the most powerful stochastic real parameter optimization algorithms in current use. DE operates through the similar computational steps as employed by a standard Evolutionary Algorithm (EA). However, unlike the traditional EAs, the DEvariants perturb the current-generation population members with the scaled differences of randomly selected and dist...

Although the Differential Evolution (DE) algorithm has been shown to be a simple yet powerful evolutionary algorithm for optimizing continuous functions, users are still faced with the problem of preliminary testing and hand-tuning of the evolutionary parameters prior to commencing the actual optimization process. As a solution, self-adaptation has been found to be highly beneficial in automati...

The determination of the optimal neural network topology is an important aspect when using neural models. Due to the lack of consistent rules, this is a difficult problem, which is solved in this paper using an evolutionary algorithm namely Differential Evolution. An improved, simple, and flexible selfadaptive variant of Differential Evolution algorithm is proposed and tested. The algorithm inc...

0020-0255/$ see front matter 2011 Elsevier Inc doi:10.1016/j.ins.2011.09.001 ⇑ Corresponding author. E-mail address: [email protected] (Y. Wang). Differential evolution (DE) is a class of simple yet powerful evolutionary algorithms for global numerical optimization. Binomial crossover and exponential crossover are two commonly used crossover operators in current popular DE. It is noteworthy that...

Theoretical analysis of the dynamics of evolutionary algorithms is believed to be very important to understand the search behavior of evolutionary algorithms and to develop more efficient algorithms. In this paper we investigate the dynamics of a canonical Differential Evolution (DE) algorithm with DE/rand/1 type mutation and binomial crossover. Differential Evolution (DE) is well-known as a si...

Setting the mutation rate for an evolutionary algorithm (EA) is confounded by many issues. Here we investigate mutation rates mainly in the context of large-population-parallelism. We justify the notion that high rates achieve better results, using underlying theory which notices that parallelization favourably alters the fitness distribution of a mutation operator. We derive an expression whic...

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...