Different strategies for defining the relationship between feasible and infeasible individuals in evolutionary algorithms can provide with very different results when solving numerical constrained optimization problems. This paper proposes a novel EA to balance the relationship between feasible and infeasible individuals to solve numerical constrained optimization problems. According to the fea...

We consider the (1+1) Evolution Strategy, a simple evolutionary algorithm for continuous optimization problems, using so-called Gaussian mutations and the 1/5-rule for the adaptation of the mutation strength. Here, the function f : R → R to be minimized is given by a quadratic form f(x) = xQx, where Q ∈ R is a positive definite diagonal matrix and x denotes the current search point. This is a n...

This paper presents an optimization approach to analyze the problems of portfolio selection for longterm investments, taking into consideration the specific target replacement ratio for defined-contribution (DC) pension scheme; the purpose is to generate an effective multi-period asset allocation that reaches an amount matching the target liability at retirement date and reduce the downside ris...

The multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) combines a mutation operator that adapts its search distribution to the underlying optimization problem with multicriteria selection. Here, a generational and two steady-state selection schemes for the MO-CMA-ES are compared. Further, a recently proposed method for computationally efficient adaptation of the search ...

The conventional weighted aggregation method is extended to realize multi-objective optimization. The basic idea is that systematically changing the weights during evolution will lead the population to the Pareto front. Two possible methods are investigated. One method is to assign a uniformly distributed random weight to each individual in the population in each generation. The other method is...

While in the absence of noise, no improvement in local performance can be gained from retaining but the best candidate solution found so far, it has been shown experimentally that in the presence of noise, operating with a non-singular population of candidate solutions can have a marked and positive effect on the local performance of evolution strategies. So as to determine the reasons for the ...

Differential evolution, termed DE, is a novel and rapidly developed evolution computation in recent years. There are some advantages of DE, including simple structure, easy use and rapid convergence speed. Besides, DE can be also applied on the complex optimization problem. However, there are some issues, such as premature convergence and stagnation, remaining in DE algorithm. To overcome those...

The paper examines the efficiency of soft computing techniques in structural optimization, in particular algorithms based on evolution strategies combined with neural networks, for solving large-scale, continuous or discrete structural optimization problems. The proposed combined algorithms are implemented both in deterministic and reliability based structural optimization problems, in an effor...

Many classification algorithms use the concept of distance or similarity between patterns. Previous work has shown that it is advantageous to optimize general Euclidean distances (GED). In this paper, data transformations are optimized instead. This is equivalent to searching for GEDs, but can be applied to any learning algorithm, even if it does not use distances explicitly. Two optimization t...

Similar to Genetic Algorithms, Evolution Strategies (ESs) are algorithms which imitate the principles of natural evolution as a method to solve parameter optimization problems. The development of Evolution Strategies from the rst mutation{selection scheme to the reened (,){ES including the general concept of self{adaptation of the strategy parameters for the mutation variances as well as their ...