scholarly journals Genetic algorithms - variable size populations of chromosomes. An adaptive approach

2018 ◽  
Vol XIX (1) ◽  
pp. 393-399
Author(s):  
Maniu R
Keyword(s):  
Genetic Algorithms ◽  
Negative Impact ◽  
Large Population ◽  
Search Space ◽  
Global Optimum ◽  
Small Population ◽  
Local Optimum ◽  
Variable Size ◽  
Algorithm Operation

The size of the chromosome population is an essential parameter of genetic algorithms. A large population involves a large amount of calculations but provides a complete scroll of the search space and the increased probability of generating a global optimum. A small population size, through the small number of operations required, causes a quick run of the algorithm, with increasing the probability of detecting a local optimum to the detriment of the global one. This paper proposes the use of an adaptive, variable size of chromosome population. We will demonstrate that this approach leads to an acceleration of the algorithm operation, without having a negative impact on the quality of provided solutions.

scholarly journals Utilizing Model Knowledge for Design Developed Genetic Algorithm to Solving Problem

2018 ◽  
Vol 3 (2) ◽  
pp. 172
Author(s):  
Hamidreza Salmani mojaveri
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Parallel Machines ◽  
Job Shop ◽  
Optimization Problems ◽  
Premature Convergence ◽  
Local Optimum ◽  
Scheduling Problems ◽  
Algorithm Implementation

One of the discussed topics in scheduling problems is Dynamic Flexible Job Shop with Parallel Machines (FDJSPM). Surveys show that this problem because of its concave and nonlinear nature usually has several local optimums. Some of the scheduling problems researchers think that genetic algorithms (GA) are appropriate approach to solve optimization problems of this kind. But researches show that one of the disadvantages of classical genetic algorithms is premature convergence and the probability of trap into the local optimum. Considering these facts, in present research, represented a developed genetic algorithm that its controlling parameters change during algorithm implementation and optimization process. This approach decreases the probability of premature convergence and trap into the local optimum. The several experiments were done show that the priority of proposed procedure of solving in field of the quality of obtained solution and convergence speed toward other present procedure.

Genetic Algorithms for Decision-Making in Cognitive Radio Networks

2013 ◽  
pp. 247-263
Author(s):  
Tommy Hult ◽  
Abbas Mohammed
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Genetic Algorithms ◽  
Cognitive Radio ◽  
Search Space ◽  
Radio Spectrum ◽  
Dynamic Decision Making ◽  
Optimal Point ◽  
Optimization Search

Efficient use of the available licensed radio spectrum is becoming increasingly difficult as the demand and usage of the radio spectrum increases. This usage of the spectrum is not uniform within the licensed band but concentrated in certain frequencies of the spectrum while other parts of the spectrum are inefficiently utilized. In cognitive radio environments, the primary users are allocated licensed frequency bands while secondary cognitive users dynamically allocate the empty frequencies within the licensed frequency band according to their requested QoS (Quality of Service) specifications. This dynamic decision-making is a multi-criteria optimization problem, which the authors propose to solve using a genetic algorithm. Genetic algorithms traverse the optimization search space using a multitude of parallel solutions and choosing the solution that has the best overall fit to the criteria. Due to this parallelism, the genetic algorithm is less likely than traditional algorithms to get caught at a local optimal point.

Global search in single-solution-based metaheuristics

2020 ◽  
Vol 54 (3) ◽  
pp. 275-296 ◽  
Author(s):  
Najmeh Sadat Jaddi ◽  
Salwani Abdullah
Keyword(s):  
Search Space ◽  
Population Based ◽  
Global Optimum ◽  
Global Search ◽  
Hill Climbing ◽  
Search Process ◽  
Local Optimum ◽  
Content Type ◽  
Solution Algorithms ◽  
Single Solution

PurposeMetaheuristic algorithms are classified into two categories namely: single-solution and population-based algorithms. Single-solution algorithms perform local search process by employing a single candidate solution trying to improve this solution in its neighborhood. In contrast, population-based algorithms guide the search process by maintaining multiple solutions located in different points of search space. However, the main drawback of single-solution algorithms is that the global optimum may not reach and it may get stuck in local optimum. On the other hand, population-based algorithms with several starting points that maintain the diversity of the solutions globally in the search space and results are of better exploration during the search process. In this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.Design/methodology/approachIn this method, different starting points in initial step, searching locally in neighborhood of each solution, construct a global search in search space for the single-solution algorithm.FindingsThe proposed method was tested based on three single-solution algorithms involving hill-climbing (HC), simulated annealing (SA) and tabu search (TS) algorithms when they were applied on 25 benchmark test functions. The results of the basic version of these algorithms were then compared with the same algorithms integrated with the global search proposed in this paper. The statistical analysis of the results proves outperforming of the proposed method. Finally, 18 benchmark feature selection problems were used to test the algorithms and were compared with recent methods proposed in the literature.Originality/valueIn this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.

Genetic Range Genetic Algorithms to Obtain Quasi-Optimum Solutions

2003 ◽  
Author(s):  
Masao Arakawa ◽  
Tomoyuki Miyashita ◽  
Hiroshi Ishikawa
Keyword(s):  
Genetic Algorithms ◽  
Fitness Function ◽  
New Product ◽  
Global Optimum ◽  
Test Problems ◽  
Local Optimum ◽  
Optimum Solution ◽  
Product Response ◽  
Peaked Function ◽  
Global Optimum Solution

In some cases of developing a new product, response surface of an objective function is not always single peaked function, and it is often multi-peaked function. In that case, designers would like to have not oniy global optimum solution but also as many local optimum solutions and/or quasi-optimum solutions as possible, so that he or she can select one out of them considering the other conditions that are not taken into account priori to optimization. Although this information is quite useful, it is not that easy to obtain with a single trial of optimization. In this study, we will propose a screening of fitness function in genetic algorithms (GA). Which change fitness function during searching. Therefore, GA needs to have higher flexibility in searching. Genetic Range Genetic Algorithms include a number of searching range in a single generation. Just like there are a number of species in wild life. Therefore, it can arrange to have both global searching range and also local searching range with different fitness function. In this paper, we demonstrate the effectiveness of the proposed method through a simple benchmark test problems.

scholarly journals Properties of Gray and Binary Representations

2004 ◽  
Vol 12 (1) ◽  
pp. 47-76 ◽  
Author(s):  
Jonathan Rowe ◽  
Darrell Whitley ◽  
Laura Barbulescu ◽  
Jean-Paul Watson
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Space ◽  
Gray Code ◽  
Global Optimum ◽  
Gray Codes ◽  
Neighborhood Structure ◽  
Local Optima ◽  
Vertex Set

Representations are formalized as encodings that map the search space to the vertex set of a graph. We define the notion of bit equivalent encodings and show that for such encodings the corresponding Walsh coefficients are also conserved. We focus on Gray codes as particular types of encoding and present a review of properties related to the use of Gray codes. Gray codes are widely used in conjunction with genetic algorithms and bit-climbing algorithms for parameter optimization problems. We present new convergence proofs for a special class of unimodal functions; the proofs show that a steepest ascent bit climber using any reflected Gray code representation reaches the global optimum in a number of steps that is linear with respect to the encoding size. There are in fact many different Gray codes.Shifting is defined as a mechanism for dynamically switching from one Gray code representation to another in order to escape local optima. Theoretical results that substantially improve our understanding of the Gray codes and the shifting mechanism are presented. New proofs also shed light on the number of unique Gray code neighborhoods accessible via shifting and on how neighborhood structure changes during shifting. We show that shifting can improve the performance of both a local search algorithm as well as one of the best genetic algorithms currently available.

New Hardware Engine for New Genetic Algorithms

2012 ◽  
Vol 490-495 ◽  
pp. 1831-1838
Author(s):  
Fariborz Ahmadi ◽  
Reza Tati
Keyword(s):  
Genetic Algorithm ◽  
Genetic Diversity ◽  
Genetic Algorithms ◽  
Hardware Implementation ◽  
Global Optimum ◽  
Local Optimum ◽  
Genetic Operators ◽  
Soft Computing Method ◽  
Software Implementations ◽  
Computing Method

Genetic algorithm is a soft computing method that works on set of solutions. These solutions are called chromosome and the best one is the absolute solution of the problem. The main problem of this algorithm is that after passing through some generations, it may be produced some chromosomes that had been produced in some generations ago that causes reducing the convergence speed. From another respective, most of the genetic algorithms are implemented in software and less works have been done on hardware implementation. Our work implements genetic algorithm in hardware that doesn’t produce chromosome that have been produced in previous generations. In this work, most of genetic operators are implemented without producing iterative chromosomes and genetic diversity is preserved. Genetic diversity causes that not only don’t this algorithm converge to local optimum but also reaching to global optimum. Without any doubts, proposed approach is so faster than software implementations. Evaluation results also show the proposed approach is faster than hardware ones.

Numerical Control Interpolation Algorithm of Aspheric Surface Based on the Genetic Algorithms and Neural Network

2007 ◽  
Vol 364-366 ◽  
pp. 25-29
Author(s):  
Fei Hu Zhang ◽  
D.J. Chen ◽  
L.J. Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Global Optimum ◽  
Numerical Control ◽  
Local Optimum ◽  
Interpolation Algorithm ◽  
Aspheric Surfaces ◽  
The Neural Network ◽  
Circular Curves ◽  
Circular Curve

When the Neural Network model is used to interpolate the non-circular curves, there are shortcomings of converging slowly and getting into the local optimum easily. A novel numerical control interpolation algorithm based on the GA (Genetic Algorithms) and NN (Neural Network) was introduced for the ultra-precision machining of aspheric surfaces. The algorithm integrated the global searching of GA with the parallel processing of NN, enhanceed the convergence speed and found the global optimum. At the end, the quintic non-circular curve was taken as an example to do the emulation and experiment. The results prove that this algorithm can fit the non-circular curve accurately, improve the precision of numerical control interpolation and reduce the number of calculating and interpolation cycles.

A Genetic Algorithm Based Optimization for Laminated Dies Manufacturing

2010 ◽  
Author(s):  
Hossein Ahari ◽  
Amir Khajepour ◽  
Sanjeev Bedi
Keyword(s):  
Genetic Algorithms ◽  
Processing Time ◽  
Optimization Problem ◽  
New Technologies ◽  
Cnc Machining ◽  
Manufacturing Cost ◽  
Local Optimum ◽  
Reliable Solution ◽  
Niching Method

Laminated tooling is one of the new technologies which helps companies to manufacture parts with lower costs and higher accuracy. It is base on dividing entire CAD model of the part to slices and then cutting each layer profile utilizing laser cut or other techniques. Finally the layers are stacked together to make the final product. CNC machining removes the extra material and brings the part to the specific tolerances. In order to minimize the manufacturing cost, one option is reduction in the amount of the extra material and the number of slices likewise. This is considered as an optimization problem in this research. Then a genetic algorithms (G.A.) based method is offered to solve this optimization problem. However, as a common problem in most instances of genetic algorithms, premature convergence prevents system to continue searching for a more reliable solution after finding a local optimum. To address this problem, a novel niching method is presented in this paper. Results show a significant improvement in the quality of the solution as well as a considerable reduction in processing time.

scholarly journals Resident’s Perception Towards Environmental Impact on Human Wellbeing Near the Quarry Mining Vicinity

2019 ◽  
Vol 266 ◽  
pp. 01002
Author(s):  
Wan Hasmirah Wan Ibrahim ◽  
Emma Marinie Ahmad Zawawi ◽  
Khalida Mohd Sukur ◽  
Julitta Yunus ◽  
Norfashiha Hashim
Keyword(s):  
Negative Impact ◽  
Large Population ◽  
Life Quality ◽  
Preventive Measure ◽  
Well Being ◽  
Health Condition ◽  
Air Pollutant ◽  
Human Wellbeing ◽  
Quarry Mining

This study investigates the life quality of residents near the quarry mining vicinity. There are still large population found stayed within this area. The negative impact of quarry mining activities such as health problem and air pollution are among the variables that influence the human well being. The objective of this study is to investigate the experience from the residents towards the quality of air near their residential and to provide a suitable preventive measure in order to reduce this air pollutant issue. A set of questionnaire was distributed to the community at Bandar Saujana Putra and Taman Kajang Perdana, in Selangor. Residents were randomly selected to participate in this study. The study reveals the community’s problem related to health condition and safety. It is anticipated that this study could assist the residence and authorities in improving the quality of air in this area by providing the suitable preventive measure.

Advanced Genetic Algorithms Based on Adaptive Partitioning Method

2007 ◽  
Vol 11 (6) ◽  
pp. 677-680 ◽  
Author(s):  
Chang-Wook Han ◽  
◽  
Hajime Nobuhara ◽  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Global Optimum ◽  
Bench Mark ◽  
Adaptive Genetic Algorithm ◽  
Local Optima ◽  
Test Function ◽  
Adaptive Partitioning ◽  
Stochastic Optimization Algorithm

Genetic algorithms (GA) are well known and very popular stochastic optimization algorithm. Although, GA is very powerful method to find the global optimum, it has some drawbacks, for example, premature convergence to local optima, slow convergence speed to global optimum. To enhance the performance of the GA, this paper proposes an adaptive genetic algorithm based on partitioning method. The partitioning method, which enables a genetic algorithm to find a solution very effectively, adaptively divides the search space into promising sub-spaces to reduce the complexity of optimization. This partitioning method is more effective as the complexity of the search space is increasing. The validity of the proposed method is confirmed by applying it to several bench mark test function examples and a traveling salesman problem.

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