Following a number of recent papers investigating the possibility of optimal comparison-based optimization algorithms for a given distribution of probability on fitness functions, we (i) discuss the comparison-based constraints (ii) choose a setting in ...

We consider a formulation of the biobjective soft graph coloring problem so as to simultaneously minimize the number of colors used as well as the number of edges that connect vertices of the same color. We aim to evolve hyperheuristics for this class ...

Parameter control in evolutionary algorithms (EAs) has been shown to be beneficial; however, the control of offspring size has so far received very little attention. This paper introduces Futility-Based Offspring Sizing (FuBOS), a method for controlling ...

In this paper, we investigate the value of a new selection mechanism that is inspired from biochemistry, namely, vesicular selection. We test its effectiveness when used in evolutionary algorithms on a number of benchmark problems in both static and ...

This paper deals with the multi-objective definition of video compression and it solving using the NSGA-II algorithm. We define the video compression as a problem including two competing objectives and we try to find a set of near-optimal solutions so ...

This paper presents a novel evolutionary approach to solve numerical optimization problems, called Adaptive Evolution (AEv). AEv is a new micro-population-like technique because it uses small populations (less than 10 individuals). The two main ...

In this paper, we present a Fuzzy Influence Function for the CAEP model (Cultural Algorithms with Evolutionary Programming) proposed by Chung [1] and extended by Zhu [6], applied to real-valued function optimization. The proposal makes use of a Fuzzy ...

The ECGA is a competent Genetic Algorithm that uses a probabilistic model to learn the linkage among variables and then uses this information to solve hard problems using polynomial resources. However, in order to detect the linkage, the ECGA needs to ...

This paper compares several metaheuristics on the balanced graph bisection problem and identifies their relative performance on structured test graphs. For this purpose, a Simple implementation of a graph Bisection Iterated Local Search algorithm (SBILS)...

Our aim in this paper is to analyse the evolvability of diverse coding conversion operators in an instance of the states based evolutionary algorithm (SEA). Since the representation of solutions or the selection of the best encoding during the ...

This article presents the implementation of a bio-inspired algorithm, which is the algorithm of particle swarm optimization (PSO) in with the objective of minimizing the function of conformational energy ECEPP/3 for the protein folding problem (PFP) for ...

The velocity term of Particle Swarm Optimizer (PSO) easily results in particles' dispersing and thus decreasing in computation precision. In view of PSO, the simplex-based Particle Swarm Optimizer (Simplex PSO) is derived from the Nelder-Mead simplex ...

In this paper, we propose an alternative strategy of adapting the inertia weight parameter during the course of particle swarm optimization, by means of a non-monotonic inertia weight function of time. Results demonstrate that an oscillating inertia ...

Particle Swarm Optimization (PSO) has been proven to be a fast and effective search algorithm capable of solving complex and varied problems. To date numerous swarm topologies have been proposed and investigated as a means of increasing the ...

Dynamic analyses of canonical particle swarm optimization (PSO) have indicated that parameter values of phi_max = 4.1 and constriction coefficient chi = 0.729 provide adequate exploration and prevent swarm explosion. This paper shows by example that ...

A recent theoretical investigation of modular representations shows that certain modularizations can introduce a distance bias into a landscape. This was a static analysis, and empirical investigations were used to connect formal results to performance. ...

Recent work in test based coevolution has focused on employing ideas from multi-objective optimization in coevolutionary domains. So called Pareto coevolution treats the coevolving set of test cases as objectives to be optimized in the sense of multi-...

Geometric Particle Swarm Optimization (GPSO) is a recently introduced formal generalization of traditional Particle Swarm Optimization (PSO) that applies naturally to both continuous and combinatorial spaces. Differential Evolution (DE) is similar to ...

The critical functionality of many software applications relies on code that performs mathematically complex computations. However, such code is often difficult to test owing to the compound datatypes used and complicated mathematical operations ...

Statistical Disclosure Control is the collective name for a range of tools that data providers such as government departments use to protect the confidentiality of individuals or organizations. When the published tables contain magnitude data such as ...