Genetic Search Approach Research Articles

Enhanced Genetic Algorithm with K-Means for the Clustering Problem

In this paper, an algorithm for the clustering problem using a combination of the genetic algorithm with the popular K-Means greedy algorithm is proposed. The main idea of this algorithm is to use the genetic search approach to generate new clusters using the famous two-point crossover and then apply the K-Means technique to further improve the quality of the formed clusters in order to speed up the search process. Experimental results demonstrate that the proposed genetic algorithm combined with K-Means converges faster while producing the same quality of the clustering compared to the standard genetic algorithm.

On-line evolutionary optimization of an industrial fed-batch yeast fermentation process

This paper presents two genetic algorithms based on optimization methods to maximize biomass concentration, and to minimize ethanol formation. The objective function is maximized according to the values of feed flow rate, using genetic search approaches. Five case studies were carried out for different initial conditions, which strongly influence the optimal profiles of feed flow rate for the fermentation process. The ethanol and glucose disturbance effects were examined to stress the effectiveness of proposed approaches. The proposed genetic approaches were implemented for an industrial scale baker’s yeast fermentor which produces Saccharomyces cerevisiae known as baker’s yeast. The results show that optimal feed flow rate was obtained in a satisfactory and successful way for fed-batch fermentation process.

The economic lot scheduling problem: A pure genetic search approach

The economic lot scheduling problem (ELSP) is the challenge of accommodating several products to be produced on a single machine in a cyclical pattern. A solution involves determining the repetitive production schedule for N products with a goal of minimizing the total of setup and holding costs. We develop the genetic lot scheduling (GLS) procedure. This method combines an extended solution structure with a new item scheduling approach, allowing a greater number of potential schedules to be considered while being the first to explicitly state the assignment of products to periods as part of the solution structure. We maintain efficient solution feasibility determination, a problematic part of ELSP solution generation and a weakness of several other methods, by employing simple but effective sequencing rules that create “nested” schedules. We create a binary chromosomal representation of the new problem formulation and utilize a genetic algorithm to efficiently search for low cost ELSP solutions. The procedure is applied to a benchmark problem suite from the literature, including Bomberger's stamping problem [Bomberger, A dynamic programming approach to a lot scheduling problem. Management Science 1966; 12:778–84], a problem that has been under investigation since the mid 1960's. The genetic lot scheduling procedure produces impressive results, including the best solutions obtained to date on some problems. Scope and purpose The need to utilize a single facility to produce several items is a common situation. We study the fundamental form of this situation, the economic lot scheduling problem (ELSP). The ELSP involves determining the minimal cost production schedule for several items that must be produced on a single machine while meeting all demands for each product. Setup costs and times are incurred each time the machine is changed (setup) to produce a different item. Holding costs are incurred based on the average amount of an item held in inventory. We must determine how much of each item should be made in each production run as well as the order of production. Examples of ELSP situations include producing several different colors of paint on the same equipment, and producing several types of stamped metal parts with the same stamping press. Since the ELSP is NP-complete, research, dating back to the 1950's, has focused on developing efficient heuristics that yield quality solutions. Our purpose is to develop an efficient heuristic search procedure for the ELSP that addresses shortcomings of previous methods. We present an extended and more definitive solution structure than previous ELSP methods, one that allows a direct mapping of solution parameters to an implementable production schedule. In addition, we address solution feasibility testing issues and structure our problem formulation in a manner that allows efficient search via a genetic algorithm (GA). We create a non-hybridized GA-based procedure that produces ELSP solutions of excellent quality, in some cases the best solutions to date.

PERMUTATION-BASED GENETIC, TABU, AND VARIABLE NEIGHBORHOOD SEARCH HEURISTICS FOR MULTIPROCESSOR SCHEDULING WITH COMMUNICATION DELAYS

The multiprocessor scheduling problem with communication delays that we consider in this paper consists of finding a static schedule of an arbitrary task graph onto a homogeneous multiprocessor system, such that the total execution time (i.e. the time when all tasks are completed) is minimum. The task graph contains precedence relations as well as communication delays (or data transferring time) between tasks if they are executed on different processors. The multiprocessor architecture is assumed to contain identical processors connected in an arbitrary way, which is defined by a symmetric matrix containing minimum distances between every two processors. The solution is represented by a feasible permutation of tasks. In order to obtain the objective function value (i.e. schedule length, makespan), the feasible permutation has to be transformed into the actual schedule by the use of some heuristic method. For solving this NP-hard problem, we develop basic tabu search and variable neighborhood search heuristics, where various types of reduced Or-opt-like neighborhood structures are used for local search. A genetic search approach based on the same solution space is also developed. Comparative computational results on random graphs with up to 500 tasks and 8 processors are reported. On average, it appears that variable neighborhood search outperforms the other metaheuristics. In addition, a detailed performance analysis of both the proposed solution representation and heuristic methods is presented.

A genetic search approach to unfalsified PI control design for a weigh belt feeder

AbstractThis paper proposes and experimentally demonstrates an approach to automated PI tuning for an industrial weigh belt feeder that is based on unfalsified control concepts. Unfalsified control is used here as a means of using either open‐ or closed‐loop test data to identify a subset of controllers (from an initial set) that is not proved to violate the multiple objectives specified by the control engineer. A novel feature of the unfalsified approach is that it allows controllers to be eliminated from consideration by predicting their performance without actually inserting the controllers in the loop. In addition, this methodology does not require an explicit model. However, in practice, it does require some closed‐loop experimentation to determine the cost functions used to perform the unfalsification. When the unfalsified PI autotuning approach is applied to the industrial weigh belt feeder, it is able to successfully identify a subset of PI control laws that meets the performance specs. A key feature of this paper is the use of a genetic search algorithm to reduce the computational requirements of unfalsified control, especially when the initial set of controllers is large. Copyright © 2001 John Wiley & Sons, Ltd.

FACE DETECTION AND VERIFICATION USING GENETIC SEARCH

We consider the problem of searching for the face of a particular individual in a two-dimensional intensity image. This problem has many potential applications such as locating a person in a crowd using images obtained by surveillance cameras. There are two steps in solving this problem: first, face regions must be extracted from the image(s) (face detection) and second, candidate faces must be compared against a face of interest (face verification). Without any a-priori knowledge about the location and size of a face in an image, every possible image location and face size should be considered, leading to a very large search space. In this paper, we propose using Genetic Algorithms (GAs) for searching the image efficiently. Specifically, we use GAs to find image sub-windows that contain faces and in particular, the face of interest. Each sub-window is evaluated using a fitness function containing two terms: the first term favors sub-windows containing faces while the second term favors sub-windows containing faces similar to the face of interest. Both terms have been derived using the theory of eigenspaces. A set of increasingly complex scenes demonstrate the performance of the proposed genetic-search approach.

The genetic search approach. A new learning algorithm for adaptive IIR filtering

An "evolutionary" approach called the genetic algorithm (GA) was introduced for multimodal optimization in adaptive IIR filtering. However, the disadvantages of using such an algorithm are slow convergence and high computational complexity. Initiated by the merits and shortcomings of the gradient-based algorithms and the evolutionary algorithms, we developed a new hybrid search methodology in which the genetic-type search is embedded into gradient-descent algorithms (such as the LMS algorithm). The new algorithm has the characteristics of faster convergence, global search capability, less sensitivity to the choice of parameters, and simple implementation. The basic idea of the new algorithm is that the filter coefficients are evolved in a random manner once the filter is found to be stuck at a local minimum or to have a slow convergence rate. Only the fittest coefficient set survives and is adapted according to the gradient-descent algorithm until the next evolution. As the random perturbation will be subject to the stability constraint, the filter can always minimum in a stable manner and achieve a smaller error performance with a fast rate. The article reviews adaptive IIR filtering and discusses common learning algorithms for adaptive filtering. It then presents a new learning algorithm based on the genetic search approach and shows how it can help overcome the problems associated with gradient-based and GA algorithms.

A Genetic Search Approach to Space Layout Planning

Space layout planning is one of the most interesting, difficult and controversial area of computer-aided architectural design. Two major issues from the area are the topological assignment of space elements and the dimensioning of those space elements to meet certain criteria and constraints. Critical difficulties include the complexity of design information involved and the combinatorial explosion of alternatives. This paper introduces a design method which uses the evolutionary concept as a mechanism for controlling search over the huge design space. Schemas are introduced to formulate and interpret design information. The Pareto optimization concept is used in the solution of space layout planning as a multi-criteria optimization problem. An example is presented.

Solving fixed configuration problems with genetic search

Configuring a design from parts out of catalogs requires searching large numbers of combinations of parts. Optimizing search techniques are not practical for searching for parts in catalogus because the computation time is often excessive. Rule-based approaches suffer from being domain specific and thus are not appropriate for the general solution methods sought for in this work. The research reported in this paper found that a satisficing technique based on genetic search is a feasible alternative. It shows how catalog search can be solved with a genetic-search approach. It was discovered that encoding catalogs without any regard to part features did not reduce the effectiveness of the technique. The performance of genetic search also compares vary favorably with branch and bound.