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The idea of mimicking processes of organic evolution on computers and using such algorithms for solving adaptation and optimization tasks can be traced back to the Sixties. Genetic Algorithms (GA), Evolutionary Programming (EP), and Evolution Strategies (ES), the still vivid different strata of this idea, have not only survived until now, but have become an important tool within what has been called...
One of the well-known problems in evolutionary search for solving optimization problem is the premature convergence. The general constrained optimization techniques such as hybrid evolutionary programming, two-phase evolutionary programming, and Evolian algorithms are not safe from the same problem in the first phase. To overcome this problem, we apply the sharing function to the Evolian algorithm...
Evolution Strategies(ES) are an approach to numerical optimization that shows good optimization performance. However, according to our computer simulations, ES shows different optimization performance when a different lower bound of strategy parameters is adopted. We analyze that this is caused by the premature convergence of strategy parameters, although they are traditionally treated as “self-adaptive”...
The p-median problem is an NP-complete combinatorial optimisation problem well investigated in the fields of facility location and more recently, clustering and knowledge discovery. We show that hybrid optimisation algorithms provide reasonable speed and high quality of solutions, allowing effective trade-of of quality of the solution with computational effort. Our approach to hybridisation is a tightly...
The method of smoothing surfaces by correcting reflection lines which is commonly used in the car design industry, relies heavily on the experience of designers and often involves very tedious work. This paper discusses how genetic algorithms can be used to alleviate this problem by providing alternative solutions under suitable constraints set by designers. Strategies for designing genetic codes,...
In this paper, we study the relationship between learning and evolution in a simple abstract model, where neural networks capable of learning are evolved through genetic algorithms (GAs). The connective weights of individuals’ neural networks undergo modification, i.e., certain characters will be acquired, through their lifetime learning. By setting various rates for the heritability of acquired characters,...
Evolutionary programming (EP) has been widely used in numerical optimization in recent years. The adaptive parameters, also named step size control, in EP play a significant role which controls the step size of the objective variables in the evolutionary process. However, the step size control may not work in some cases. They are frequently lost and then make the search stagnate early. Applying the...
In this paper, we discuss an approach to an operator scheduling problem in a large organization over time with the aim of maintaining service quality and reducing total labor costs. We propose a genetic algorithm (GA) with a parameterized fitness function inspired by homotopy methods and with null mutation to handle a variable number of operators. The proposed method is applied to the practical problem...
We report key algorithmic specific features involved in the evolutionary radial network problem solution. We focus on the dimensionality problem of large-scale networks and on the singularities of the radial topology search space. We (1) report the difficulties of the canonical genetic algorithm in handling network topology constraints, and (2) present both the genotype information structure and the...
The allocation of office space in any large institution is usually a problematical issue, which often demands a substantial amount of time to perform manually. The result of this allocation affects the lives of whoever makes use of the space. In the higher education sector in the UK, space is becoming an increasingly precious commodity. Student numbers have risen significantly over the last few years...
K-nearest neighbour (KNN) algorithm in combination with a genetic algorithm were applied to a medical fraud detection problem. The genetic algorithm was used to determine the optimal weighting of the features used to classify General Practitioners’ (GP) practice profiles. The weights were used in the KNN algorithm to identify the nearest neighbour practice profiles and then two rules (i.e. the majority...
This paper proposes a genetic-algorithm-based approach for finding a compact reference set used in nearest neighbor classification. The reference set is designed by selecting a small number of reference patterns from a large number of training patterns using a genetic algorithm. The genetic algorithm also removes unnecessary features. The reference set in our nearest neighbor classification consists...
The cellular genetic algorithm (CGA) combines GAs with cellular automata by spreading an evolving population across a pseudo-landscape. In this study we use insights from ecology to introduce new features, such as disasters and connectivity changes, into the algorithm. We investigate the performance and behaviour of the algorithm on standard GA hard problems. The CGA has the advantage of avoiding...
Unsupervised learning algorithms realizing topographic mappings are justified by neurobiology while they are useful for multivariate data analysis. In contrast to supervised learning algorithms unsupervised neural networks have their objective function implicitly defined by the learning rule. When considering topographic mapping as an optimization problem, the presence of explicitly defined objective...
In this paper we report results for the prediction of thermo-dynamic properties based on neural networks, evolutionary algorithms and a combination of them. We compare backpropagation trained networks and evolution strategy trained networks with two physical models. Experimental data for the enthalpy of vaporization were taken from the literature in our investigation. The input information for both...
Cellular Automata architectures are attractive due to their fine grain parallelism, simple computational structures and local routing resources. Some researchers have used genetic algorithms to find CA that perform useful computations. The inherently parallel cellular automata model as well as the genetic algorithm are poorly suited to implementation on general purpose microprocessor based systems...
Island Parallel GA divides a population into subpopulations and assigns them to processing elements on a parallel computer. Then each subpopulation searches the optimal solution independently, and exchanges individuals periodically. This exchange operation is called migration. In this research, we propose a new algorithm that migrants are exchanged asynchronously among multiform subpopulations which...
An efficient approach to solve multiple sequence alignment problem is presented in this paper. This approach is based on parallel genetic algorithm(PGA) that runs on a networked parallel environment. The algorithm optimizes an objective function ‘weighted sums of pairs’ which measures alignment quality. Using isolated independent subpopulations of alignments in a quasi evolutionary manner this approach...
In this paper, an algorithm is presented for learning concept classification rules. It is a hybrid between evolutionary computing and inductive logic programming (ILP). Given input of positive and negative examples, the algorithm constructs a logic program to classify these examples. The algorithm has several attractive features including the ability to explicitly use background (user-supplied) knowledge...
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