A FITNESS-BASED MULTI-PARENT CROSSOVER OPERATOR WITH PROBABILISTIC SELECTION

2012 ◽  
Vol 21 (01) ◽  
pp. 1250005
Author(s):  
SURAPONG AUWATANAMONGKOL
Keyword(s):  
Genetic Algorithms ◽  
Threshold Value ◽  
Fast Convergence ◽  
Premature Convergence ◽  
Test Functions ◽  
Crossover Operator ◽  
Solution Quality ◽  
Early Stages ◽  
Optimal Setting ◽  
Conflicting Goals

Several multi-parent crossover operators have been proposed to increase the performance of genetic algorithms. In these cases, the operators allow several parents to simultaneously take part in creating offspring. However, the operators need to find a balance between the two conflicting goals of exploitation and exploration. Strong exploitation allows fast convergence to succeed but can lead to premature convergence while strong exploration can lead to better solution quality but slower convergence. This paper proposes a new fitness based scanning multi-parent crossover operator for genetic algorithms. The new operator seeks out the optimal setting for the two goals in order to achieve the highest benefits from both. The operator uses a probabilistic selection with an incremental threshold value to allow strong exploration in the early stages of the algorithms and strong exploitation in their later stages. Experiments conducted on some test functions show that the operator can give better solution quality and more convergence consistency when compared with some other well-known multi-parent crossover operators.

scholarly journals Robust Optimal Tracking Control of a Full-Bridge DC-AC Converter

2021 ◽  
Vol 11 (3) ◽  
pp. 1211
Author(s):  
En-Chih Chang ◽  
Chun-An Cheng ◽  
Rong-Ching Wu
Keyword(s):  
Steady State ◽  
Digital Signal Processor ◽  
Tracking Control ◽  
Harmonic Distortion ◽  
Digital Signal ◽  
Fast Convergence ◽  
Solution Quality ◽  
Hybrid Strategy ◽  
Optimal Tracking ◽  
Optimal Tracking Control

This paper develops a full-bridge DC-AC converter, which uses a robust optimal tracking control strategy to procure a high-quality sine output waveshape even in the presence of unpredictable intermissions. The proposed strategy brings out the advantages of non-singular fast convergent terminal attractor (NFCTA) and chaos particle swarm optimization (CPSO). Compared with a typical TA, the NFCTA affords fast convergence within a limited time to the steady-state situation, and keeps away from the possibility of singularity through its sliding surface design. It is worth noting that once the NFCTA-controlled DC-AC converter encounters drastic changes in internal parameters or the influence of external non-linear loads, the trembling with low-control precision will occur and the aggravation of transient and steady-state performance yields. Although the traditional PSO algorithm has the characteristics of simple implementation and fast convergence, the search process lacks diversity and converges prematurely. So, it is impossible to deviate from the local extreme value, resulting in poor solution quality or search stagnation. Thereby, an improved version of traditional PSO called CPSO is used to discover global optimal NFCTA parameters, which can preclude precocious convergence to local solutions, mitigating the tremor as well as enhancing DC-AC converter performance. By using the proposed stable closed-loop full-bridge DC-AC converter with a hybrid strategy integrating NFCTA and CPSO, low total harmonic distortion (THD) output-voltage and fast dynamic load response are generated under nonlinear rectifier-type load situations and during sudden load changes, respectively. Simulation results are done by the Matlab/Simulink environment, and experimental results of a digital signal processor (DSP) controlled full-bridge DC-AC converter prototype confirm the usefulness of the proposed strategy.

Test driving three 1995 genetic algorithms: New test functions and geometric matching

1995 ◽  
Vol 1 (1) ◽  
pp. 77-104 ◽  
Author(s):  
D. Whitley ◽  
R. Beveridge ◽  
C. Graves ◽  
K. Mathias
Keyword(s):  
Genetic Algorithms ◽  
Test Functions ◽  
Geometric Matching

Robust confidence intervals applied to crossover operator for real-coded genetic algorithms

2007 ◽  
Vol 12 (8) ◽  
pp. 809-833 ◽  
Author(s):  
Domingo Ortiz-Boyer ◽  
César Hervás-Martínez ◽  
Nicolás García-Pedrajas
Keyword(s):  
Genetic Algorithms ◽  
Confidence Intervals ◽  
Crossover Operator ◽  
Real Coded Genetic Algorithms

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.

scholarly journals EVOLUTIONARY OPTIMIZATION OF RBF NETWORKS

2001 ◽  
Vol 11 (03) ◽  
pp. 287-294 ◽  
Author(s):  
E. LACERDA ◽  
A. DE CARVALHO ◽  
TERESA LUDERMIR
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Multiobjective Optimization ◽  
Optimization Criterion ◽  
Benchmark Problem ◽  
Crossover Operator ◽  
Rbf Networks ◽  
New Strategy ◽  
Free Parameters

One of the main obstacles to the widespread use of artificial neural networks is the difficulty of adequately defining values for their free parameters. This article discusses how Radial Basis Function (RBF) networks can have their parameters defined by genetic algorithms. For such, it presents an overall view of the problems involved and the different approaches used to genetically optimize RBF networks. A new strategy to optimize RBF networks using genetic algorithms is proposed, which includes new representation, crossover operator and the use of a multiobjective optimization criterion. Experiments using a benchmark problem are performed and the results achieved using this model are compared to those achieved by other approaches.

A New Crossover Operator for Assembly Sequence Planning by Genetic Algorithms

1996 ◽  
Author(s):  
Milad Fares Sebaaly ◽  
Hideo Fujimoto
Keyword(s):  
Genetic Algorithms ◽  
Decision Process ◽  
Genetic Material ◽  
Assembly Sequence Planning ◽  
Assembly Sequence ◽  
Crossover Operator ◽  
Optimal Sequence ◽  
Sequence Planning ◽  
Complete Set ◽  
Maximum Transmission

Abstract Assembly Sequence Planning (ASP) is the generation of the best or optimal sequence to assemble a certain product, given its design files. Although many planners were introduced in research to solve this problem automatically, it is still solved manually in many advanced assembly firms. The reason behind this is that most introduced planners are very sensitive to large increases in product parts. In fact, most of these planners seek the exact solution, while performing a part basis decision process. As a result, they are trapped in tedious and exhaustive search procedures, which make them inefficient and sometimes obsolete. To overcome these difficulties, Sebaaly and Fujimoto (1996) introduced a new concept of ASP based on Genetic Algorithms application, where the search procedure is performed on a sequence population basis rather than a part basis, and a best sequence is generated without searching the complete set of potential candidates. This paper addresses the problem of improving the GA performance for assembly application, by introducing a new crossover operator. The genetic material can be divided and classified as ‘good’ or ‘bad’. The new crossover insures the maximum transmission of ‘good’ features from one generation to another. This results in a faster GA convergence. The performance of the new algorithm is compared with that of the ordinary matrix crossover for a modified industrial example, where it proved to be faster and more efficient.

Operator Control Parameters and Fine Tuning of Genetic Algorithms (GAs)

2021 ◽  
pp. 115-127
Keyword(s):  
Genetic Algorithms ◽  
Black Box ◽  
Fine Tuning ◽  
Control Parameters ◽  
Solution Quality ◽  
Internal Working ◽  
Size Number ◽  
Crossover Probability ◽  
The Mind ◽  
Operator Control

Genetic algorithms (GAs) are heuristic, blind (i.e., black box-based) search techniques. The internal working of GAs is complex and is opaque for the general practitioner. GAs are a set of interconnected procedures that consist of complex interconnected activity among parameters. When a naive GA practitioner tries to implement GA code, the first question that comes into the mind is what are the value of GA control parameters (i.e., various operators such as crossover probability, mutation probability, population size, number of generations, etc. will be set to run a GA code)? This chapter clears all the complexities about the internal interconnected working of GA control parameters. GA can have many variations in its implementation (i.e., mutation alone-based GA, crossover alone-based GA, GA with combination of mutation and crossover, etc.). In this chapter, the authors discuss how variation in GA control parameter settings affects the solution quality.

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