This paper introduces SofEA, a pool-based architecture built over CouchDB for distributing evolutionary algorithms (EAs) across computer network in an asynchronous and decentralized way. Clients perform different functions (evaluation, reproduction, ...

The problem of effectively and efficiently finding the global optimum of a function by using evolutionary algorithms is current and pertinent, and two of the evolutionary techniques that have received significant attention in the literature are Particle ...

This paper proposes a novel differential evolution (DE) algorithm with random walk (DE-RW). Random walk is a famous phenomenon universally exists in nature and society. As random walk is an erratic movement that can go in any direction and go to any ...

We have recently proposed a highly effective method for speeding up metaheuristics in solving combinatorial optimization problems called pattern reduction (PR). It is, however, limited to problems with solutions that are either binary or integer ...

Many relevant industrial optimization tasks feature more than just one quality criterion. State-of-the-art multi-criteria optimization algorithms require a relatively large number of function evaluations (usually more than 10^5) to approximate Pareto ...

In this paper, the performance of two classical algorithms (simulated annealing and a discrete artificial bee colony) are compared on the redistricting problem, using a real example in Mexico and highlighting the superiority of the latter.

This paper presents a novel surrogate-assisted evolutionary programming (EP) method for high dimensional constrained black-box optimization with many black-box inequality constraints. A cubic radial basis function (RBF) surrogate is used and the ...

The goal of linkage learning in evolutionary algorithms is to identify the interactions between variables of a problem. Knowing the linkage information helps search algorithms to find the optimum solution efficiently and reliably in hard problems. This ...

This work proposes a controlled stochastic difference equation model of scheduling, with quadratic cost criteria, for cancer chemotherapy. By reducing the problem to quadratic control optimization and introducing a random search algorithm, we seek an ...

With recent advances in mobile computing, swarm robotics has demonstrated its utility in countless situations like recognition, surveillance, and search and rescue. This paper presents a novel approach to optimize the position of a swarm of robots to ...

The continuity of edges is very important in some image processing applications but the detection of continuous edges is a very hard problem and is particularly time consuming in noisy images. The Canonical Particle Swarm Optimisation (CanPSO) has been ...

Evolutionary Algorithms (EAs), when used for global optimization, can be seen as unconstrained optimization techniques. Therefore, they require an additional mechanism to incorporate constraints of any kind (i.e., inequality, equality, linear, nonlinear)...

Probabilistic model-building genetic algorithms (PMBGAs), also known as estimation of distribution algorithms (EDAs) and iterated density-estimation algorithms (IDEAs), guide the search for the optimum by building and sampling explicit probabilistic ...

Many optimization problems are multiobjective in nature in the sense that multiple, conflicting criteria need to be optimized simultaneously. Due to the conflict between objectives, usually, no single optimal solution exists. Instead, the optimum ...

This work proposes a linkage-learning niching method that improves the capability of estimation of distribution algorithms (EDAs) on reducing spurious linkages which increase problems difficulty. Concatenated parity function (CPF), a class of allelic ...

In usual Genetic Programming (GP) schemes, only the best programs survive from one generation to the next. This implies that useful code, that might be hidden inside introns in low fitness individuals, is often lost. In this paper, we propose a new ...

Common evolutionary algorithms are often inapplicable to technical design problems in the automotive industry. They cannot deal with multi-objective optimization problems, they need too many function evaluations and time and they cannot handle with a ...

The key issue for an efficient and reliable multi-objective evolutionary algorithm is the ability to converge to the True Pareto Front with the least number of objective function evaluations, while covering it as much as possible. To this purpose, in a ...

This paper presents a framework for co-adapting mobile sensors in hostile environments to allow telepresence of a distant user. The presented technique relies on cooperative co-evolution for sensor placement. It is shown that cooperative co-evolution is ...