In this paper, we propose a simple yet effective approach in surrogate-assisted genetic algorithms employing a neural network to estimate survival probabilities of individuals in selections to reduce computational cost of their fitness evaluations. A ...

We have proposed EDA-GK, Estimation of Distribution Algorithms with Graph Kernels. The EDA-GK is designed for solving graph-related problems, where individuals can be represented by graphs. By using graph kernels, the EDA-GK can be solved for graph-...

Swarm intelligence is rather a simple implementation but has a good performance in function optimization. There are a variety of instances of swarm model and has its inherent dynamic property. In this study we consider a hybrid swarm model where agents ...

In genetic algorithms, the importance of the basis for representation has been well known. In this paper, we studied the effect of a good basis in binary representation, and resultantly we could show that a good basis improves the performance of search ...

For solving multi-objective set packing problems involving constraints, this work proposes an algorithm combining an infeasible solution repair method and MOEA/D sharing the same weight vector set determining search directions in the objective space. To ...

Genetic algorithms (GA) [8] are currently one of the most widely used meta-heuristics to solve engineering problems. Furthermore, parallel genetic algorithms (pGAs) are useful to find solutions of complex optimizations problems in adequate times [16]; ...

This paper proposes different visualisation techniques to understand the behaviour of an algorithm's entities during the search process when solving multi-objective optimisation problems. A scatter plot is used to highlight the Pareto-ranking of the ...

Learning surrogates for product design and optimization is potential to capitalize on competitive market segments. In this paper we propose an approach to learn surrogates of product performance from historical clusters by using ensembles of Genetic ...

Recently, black-box differential evolution (BBDE) has been proposed to overcome the search biases and sensitivity to rotation of the classic differential evolution (DE). To date, BBDE has been studied only for the 'rand' strategy and even for this ...

This paper provides a taxonomical identification survey of classes in discrete optimization challenges that can be found in the literature including a proposed pipeline for benchmarking, inspired by previous computational optimization competitions. ...

In computer security, guidance is slim on how to prioritize or configure the many available defensive measures, when guidance is available at all. We show how a competitive co-evolutionary algorithm framework can identify defensive configurations that ...

Combinatorial optimization problems come in a wide variety of types but five common problem components can be identified. This categorization can aid the selection of interesting and diverse set of problems for inclusion in the combinatorial black-box ...

Owing to the immunity to illumination and atmospheric conditions, synthetic aperture radar (SAR) images have been the main source of data for environmental monitoring. However, it is a challenging task for change detection because of the influence of ...

Perl 6 is a recently released language that belongs to the Perl family but was actually designed from scratch, not as a refactoring of the Perl 5 codebase. Through its two-year-old (released) history, it has increased performance by several orders of ...

Robust and multi-modal optimisation are two important topics that have received significant attention from the evolutionary computation community over the past few years. However, the two topics have usually been investigated independently and there is ...

Evolutionary algorithms are cost-effective for solving real-world optimization problems, such as NP-hard and black-box problems. Before an evolutionary algorithm can be put into real-world applications, it is desirable that the algorithm was tested on a ...

In this paper, we address the problem of applying evolutionary dynamic optimization of network monitoring to highly dynamic communication network infrastructures.

One major challenge of modern communication networks is the increasing volatility due to, ...

This paper introduces a new, highly asynchronous method for surrogate-assisted optimization where it is possible to concurrently create surrogate models, evaluate fitness functions and do parameter optimization for the underlying problem, effectively ...

In recent years, planning sport training sessions with computational intelligence have been studied by many authors. Most of the algorithms were used for proposing basic and advanced training plans for athletes. In a nutshell, most of the solutions ...

One of the most relevant problems in social networks is influence maximization, that is the problem of finding the set of the most influential nodes in a network, for a given influence propagation model. As the problem is NP-hard, recent works have ...