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.

scholarly journals Statistical Design of Genetic Algorithms for Combinatorial Optimization Problems

2011 ◽  
Vol 2011 ◽  
pp. 1-17 ◽  
Author(s):  
Moslem Shahsavar ◽  
Amir Abbas Najafi ◽  
Seyed Taghi Akhavan Niaki
Keyword(s):  
Genetic Algorithms ◽  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Statistical Design ◽  
Fine Tuning ◽  
Combinatorial Optimization Problems ◽  
Crossover Probability ◽  
Project Scheduling Problem ◽  
Np Complete ◽  
Parameter Values

Many genetic algorithms (GA) have been applied to solve different NP-complete combinatorial optimization problems so far. The striking point of using GA refers to selecting a combination of appropriate patterns in crossover, mutation, and and so forth and fine tuning of some parameters such as crossover probability, mutation probability, and and so forth. One way to design a robust GA is to select an optimal pattern and then to search for its parameter values using a tuning procedure. This paper addresses a methodology to both optimal pattern selection and the tuning phases by taking advantage of design of experiments and response surface methodology. To show the performances of the proposed procedure and demonstrate its applications, it is employed to design a robust GA to solve a project scheduling problem. Through the statistical comparison analyses between the performances of the proposed method and an existing GA, the effectiveness of the methodology is shown.

scholarly journals Differential Evolution: A Survey and Analysis

2018 ◽  
Vol 8 (10) ◽  
pp. 1945 ◽  
Author(s):  
Tarik Eltaeib ◽  
Ausif Mahmood
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
State Of The Art ◽  
Population Based ◽  
Vital Role ◽  
Fine Tuning ◽  
Control Parameters ◽  
Hybrid Techniques ◽  
Global Optimizer

Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based metaheuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. In particular, we present a state-of-the-art survey of the literature on DE and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques.

MODELING THE SYNTHESIS OF TAKAGI — SUGENO — KANG FUZZY CONTROLLERS IN SOME CONTROL SYSTEMS

2021 ◽  
Vol 7 (2) ◽  
pp. 147-169
Author(s):  
Irina V. Kulikova
Keyword(s):  
Genetic Algorithms ◽  
Control System ◽  
Fuzzy Controller ◽  
Industrial Society ◽  
Control Action ◽  
Effective Control ◽  
Control Parameters ◽  
Membership Functions ◽  
Fuzzy Controllers ◽  
Takagi Sugeno

Modern challenges in a post-industrial society require further development of management systems for complex technical and technological phenomena and processes. Effective control of an object is possible if a controller, or a fuzzy controller, correctly generates the required control action. Recently, fuzzy controllers have been very popular. Fuzzy logical statements in this case help considering various nonlinear relationships. The synthesis of the fuzzy controller parameters allows for more efficient operation of the control system. A possible option for obtaining the best set of parameters for a fuzzy controller is the use of genetic algorithms for its synthesis. The use of genetic algorithms for the fuzzy controllers synthesis can lead to the fact that the elements of its parameters array will change in such a way that an incorrect value of one or more elements will occur. This situation leads to impossibility of composing membership functions for the terms of the variables of the fuzzy controller. Incorrect value formation is excluded by constructing a limited functional dependency. This paper proposes a mathematical model of the parameters of the term-set of variables of a fuzzy controller of the Takagi — Sugeno — Kang type of the zero and first orders. The authors disclose the content of the conditions and conclusions of the rule base for the fuzzy controller of the above type. As a result of the simulation, some operations of the genetic algorithm are implemented using a random number generator. Graphical models of the membership functions of the input variables of the fuzzy controller of the type under consideration clearly illustrate the occurrence of all parameters in their range of possible values. A description of the control system operation with two control parameters and one control action at the specified values of the control parameters is presented.

Behaviorism and Developments in Instructional Design and Technology

2011 ◽  
pp. 153-172 ◽  
Author(s):  
Irene Chen
Keyword(s):  
Instructional Design ◽  
Mathematical Analysis ◽  
Black Box ◽  
Design And Technology ◽  
Thought Processes ◽  
Specific Stimulus ◽  
Learning To Read ◽  
Stimulus Response ◽  
Observable Change ◽  
The Mind

The theory of behaviorism concentrates on the study of overt behaviors that can be observed and measured (Good & Brophy, 1990). In general, the behavior theorists view the mind as a “black box” in the sense that response to stimulus can be observed quantitatively, ignoring the possibility of thought processes occurring in the mind. Behaviorists believe that learning takes place as the result of a response that follows on a specific stimulus. By repeating the S-R (stimulus-response) cycle, the organism (may it be an animal or human) is conditioned into repeating the response whenever the same stimulus is present. The behavioral emphasis on breaking down complex tasks, such as learning to read, into subskills that are taught separately, has a powerful influence on instructional design. Behaviors can be modified, and learning is measured by observable change in behavior. The behavior theorists emphasize the need of objectivity, which leads to great accentuation of statistical and mathematical analysis.

scholarly journals Fine-Tuning of a Fuzzy Computed-Torque Control for a 2-DOF Robot via Genetic Algorithms

2018 ◽  
Vol 51 (13) ◽  
pp. 326-331
Author(s):  
A.O. Pizarro-Lerma ◽  
R. García-Hernández ◽  
V.A. Santibáñez
Keyword(s):  
Genetic Algorithms ◽  
Torque Control ◽  
Fine Tuning ◽  
Computed Torque Control ◽  
Computed Torque

Research on the Optimization of the Numerical Value Based on Improved Genetic Algorithm

2013 ◽  
Vol 333-335 ◽  
pp. 1256-1260
Author(s):  
Zhen Dong Li ◽  
Qi Yi Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Adaptive Genetic Algorithm ◽  
Improved Genetic Algorithm ◽  
Adaptive Genetic Algorithms ◽  
Crossover Probability ◽  
Adaptive Crossover ◽  
Searching Ability ◽  
New Algorithms ◽  
Relevance Index

For the lack of crossover operation, from three aspects of crossover operation , systemically proposed one kind of improved Crossover operation of Genetic Algorithms, namely used a kind of new consistent Crossover Operator and determined which two individuals to be paired for crossover based on relevance index, which can enhance the algorithms global searching ability; Based on the concentrating degree of fitness, a kind of adaptive crossover probability can guarantee the population will not fall into a local optimal result. Simulation results show that: Compared with the traditional cross-adaptive genetic Algorithms and other adaptive genetic algorithm, the new algorithms convergence velocity and global searching ability are improved greatly, the average optimal results and the rate of converging to the optimal results are better.

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.

The Mind as Black Box: A Simulation of Theory Building in Psychology

2000 ◽  
Vol 27 (3) ◽  
pp. 195-197 ◽  
Author(s):  
Carolyn Hildebrandt ◽  
Jennifer Oliver
Keyword(s):  
Graduate Students ◽  
Small Groups ◽  
Black Box ◽  
Scientific Investigation ◽  
Theory Building ◽  
Logical Thinking ◽  
Social Collaboration ◽  
Hands On ◽  
Black Plastic ◽  
The Mind

This activity gives students a collaborative, hands-on experience in theory building. Using the metaphor “the mind is a black box,” students work together in small groups to discover what is inside a sealed, black, plastic box. In all, 63 undergraduate and 11 graduate students evaluated the activity. Students reported that they enjoyed the activity and that it helped them learn more about the development of scientific theories; the existence of conflicting theories; and the value of logical thinking, imagination, and social collaboration in the process of scientific investigation.

Control parameters inversion using genetic algorithms applied to numerical impedance synthesis for woodwinds

2006 ◽  
Vol 120 (5) ◽  
pp. 3333-3333
Author(s):  
Laura Perichon ◽  
Olivier Carriere ◽  
Jean‐Pierre Hermand ◽  
Matthias Meyer ◽  
Philippe Guillemain
Keyword(s):  
Genetic Algorithms ◽  
Control Parameters ◽  
Parameters Inversion
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