Artificial neural networks provide a methodology for solving many types of nonlinear problems that are difficult to solve using traditional techniques. Neurogenetic hybrid systems bring together the artificial neural networks benefits and the inherent advantages of evolutionary algorithms. A functional approximation method using neuro-genetic hybrid systems is proposed in this paper. Three evol...

In recent years many efficient nature-inspired techniques (based on evolutionary strategies, particle swarm optimization, differential evolution and others) have been proposed for real-valued multimodal optimization (MMO) problems. Unfortunately, there is a lack of efficient approaches for problems with binary representation. Existing techniques are usually based on general ideas of niching. Mo...

This paper discusses the optimal node placement problem of Industrial Wireless Sensor Networks (IWSNs). Compared with the non-industrial Wireless Sensor Networks (WSNs), IWSNs have a critical requirement on the reliability of networks. Thus, a new model of the IWSNs node placement is formulated in which the reliability, cost, load constraint and scalability are taken into account. Considering i...

hardware-implemented ANN presented framework realization: ! 2 ANN ASICs with binary neurons and analog synapses ! implementing a recurrent Perceptron model ! controlled by an FPGA (framework controller) ! trained by an evolutionary algorithm ! capabale of scaling mapping to resources desired network topology We propose a framework for hardware artificial neural networks (ANN) that allows to map...

In this paper an evolutionary technique for detecting hierarchical structure of a data set is considered [4]. A linear representation of the cluster structure within the data set is used. An evolutionary algorithm evolves a population of clustering hierarchies. Proposed algorithm uses mutation and crossover as search (variation) operators. Binary tournament selection is considered. A new crosso...

This paper presents a new multi-objective evolutionary algorithm based on differential evolution. The proposed approach adopts a secondary population in order to retain the non-dominated solutions found during the evolutionary process. Additionally, the approach also incorporates the concept of ε-dominance to get a good distribution of the solutions retained. The main goal of this work was to k...

we study the problem of reconstructing binary images from four projections data in a fuzzy environment. given the uncertainly projections,w e want to find a binary image that respects as best as possible these projections. we provide an iterative algorithm based on fuzzy integer programming and linear membership functions.

An evolutionary algorithm is used to search for iterated function systems (IFS) that can encode black and white images. As the number of maps of the IFS that encodes an image cannot be known in advance, a variable-length genotype is used to represent candidate solutions. Accordingly, feasibility conditions of the maps are introduced, and special genetic operators that maintain and control their...

Contrary to indications made by prior researchers, digital logic circuits designed by artificial evolution to perform binary arithmetic tasks can generalise on inputs which were not seen during evolution. This phenomenon is demonstrated experimentally and speculatively explained in terms of the regular structure of binary arithmetic tasks and the nonoptimality of random circuits. This explanati...

The integration of Swarm Intelligence (SI) algorithms and Evolutionary algorithms (EAs) might be one of the future approaches in the Evolutionary Computation (EC). This work narrates the early research on using Stochastic Diffusion Search (SDS) – a swarm intelligence algorithm – to empower the Differential Evolution (DE) – an evolutionary algorithm – over a set of optimisation problems. The res...