With this paper we present a Multivalued Evolutionary Algorithm (MEA) which is inspired by fuzzy set theory. The genetic representation and encoding is done in such a way that no inferences can be drawn from phenotype to genotype. This representation innuences the used genetic operators. The basic operators of the algorithm will be explained and comparisons for global optimization problems with...

MOTIVATION Gene genealogies offer a powerful context for inferences about the evolutionary process based on presently segregating DNA variation. In many cases, it is the distribution of population parameters, marginalized over the effectively infinite-dimensional tree space, that is of interest. Our evolutionary forest (EF) algorithm uses Monte Carlo methods to generate posterior distributions ...

We propose a multi-objective evolutionary algorithm (MOEA), named the Hyper-volume Evolutionary Algorithm (HVEA). The algorithm is characterised by three components. First, individual fitness evaluation depends on the current Pareto front, specifically on the ratio of its dominated hyper-volume to the current Pareto front hyper-volume, hence giving an indication of how close the individual is t...

In both numerical and stochastic optimization methods, surrogate models are often employed in lieu of the expensive high-fidelity models to enhance search efficiency. In gradient-based numerical methods, the trustworthiness of the surrogate models in predicting the fitness improvement is often addressed using ad hoc move limits or a trust region framework (TRF). Inspired by the success of TRF i...

Constrained optimization evolutionary algorithm (COEA) is a mathematical programming problem frequently encountered in the field of engineering application. Solving constrained optimization problems by COEA has become an important research area of evolutionary computation in recent years. In this paper, the constrained optimization evolutionary algorithm is based on the quantum evolutionary alg...

Evolutionary algorithms (EA) are randomized heuristic search methods based on the principles of natural evolution (Banzhaf et al., 1998; Goldberg, 1989; Holland, 1975; Bäck, 1996; Koza, 1992). If we know how to describe the problem using the terminology of artificial evolution, the EAs are quite easy to apply. Actually, the search for solution(s) is transformed into a search for the best EA set...

The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a “data mining-based evolutionary fuzzy expert system” (DEFE...