We present a comparative review of Evolutionary Algorithms that generate new population members by sampling a probability distribution constructed during the optimization process. We present a unifying formulation for five such algorithms that enables ...

Considering the available body of literature on continuous EDAs, one must state that many important questions are still unanswered, e.g.: How do continuous EDAs really work, and how can we increase their efficiency further? The first question must be ...

This paper proposes a novel technique for a program evolution based on probabilistic models. In the proposed method, two probabilistic distribution models with probabilistic dependencies between variables are used together. We empirically comfirm that ...

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 ...

In dynamic environments, the optimal solution changes over time. To track the solution, an improved univariate marginal distribution algorithm (UMDA) is proposed. A transfer model is introduced to increase the diversity of population. The current information ...

This paper proposed a multi-objective evolutionary algorithm (called by GDE-EDA hereinafter). The proposed algorithm combined a generalized differential evolution (DE) with an estimation of distribution algorithm (EDA). This combination can simultaneously ...

Probabilistic model-building algorithms (PMBGAs) replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilistic model of promising solutions and (2) sampling the built model to generate new candidate solutions. PMBGAs ...

Multiobjective optimization problems with many local Pareto fronts is a big challenge to evolutionary algorithms. In this paper, two operators, biased initialization and biased crossover, are proposed to improve the global search ability of RM-MEDA, ...

Estimation of distribution algorithms are evolutionary algorithms using probabilistic techniques instead of traditional genetic operators. Recently, the application of probabilistic techniques to program and function evolution has received increasing ...

In this paper, we present an approach to overcome the scalability issues associated with instance-based learners. Our system uses evolutionary computational techniques to determine the minimal set of training instances needed to achieve good classification ...

The real options technique has emerged as an evaluation tool for investment under uncertainty. It explicitly recognizes future decisions, and the exercise strategy is based on the optimal decisions in future periods. This paper employs the optimal stopping ...

In this paper we deal with the problem of automatically optimizing the gait of a robot for forward walking speed. Each different walking surface and/or the wear and tear of the robots determines the speed of the robot. This means that a specific gait ...

A new real-coded multi-objective estimation of distribution algorithm (RCMEDA) for optimization problems with continuous variables is developed. Decision tree is used for discretization to encode conditional dependencies among variables in RCMEDA, i.e. ...

Genetic Algorithm (GA) is a kind of algorithm that simulates the process and the mechanism of the evolution. Because of its unique biologic feature and its suitability to any function, it becomes very popular and has been used in many problems in many ...

Multivariate Gaussian models are widely adopted in continuous estimation of distribution algorithms (EDAs), and covariance matrix plays the essential role in guiding the evolution. In this paper, we propose a new framework for multivariate Gaussian based ...

Probabilistic model-building algorithms (PMBGAs) replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilistic model of promising solutions and (2) sampling the built model to generate new candidate solutions. PMBGAs ...

This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated connectionist system, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary ...

This paper describes and analyzes an estimation of distribution algorithm based on dependency tree models (dtEDA), which can explicitly encode probabilistic models for permutations. dtEDA is tested on deceptive ordering problems and a number of instances ...

In this work we propose an estimation of distribution algorithm (EDA) as a new tool aiming at minimizing the total flowtime in permutation flowshop scheduling problems. A variable neighbourhood search is added to the algorithm as an improvement procedure ...