scholarly journals Development and validation of constraints handling in a Differential Evolution optimizer

2020 ◽  
Vol 12 (1) ◽  
pp. 59-66
Author(s):  
Mihai-Vladut HOTHAZIE ◽  
Georgiana ICHIM ◽  
Mihai-Victor PRICOP
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Differential Evolution ◽  
Research Work ◽  
Scaling Factor ◽  
Test Problems ◽  
Objective Functions ◽  
Constraints Handling ◽  
Two Parameters ◽  
Development And Validation

Research work requires independent, portable optimization tools for many applications, most often for problems where derivability of objective functions is not satisfied. Differential evolution optimization represents an alternative to the more complex, encryption based genetic algorithms. Various packages are available as freeware, but they lack constraints handling, while constrained optimizations packages are commercially available. However, the literature devoted to constraints treatment is significant and the current work is devoted to the implementation of such an optimizer, to be applied in low-fidelity optimization processes. The parameter free penalty scheme is adopted for implementation, and the code is validated against the CEC2006 benchmark test problems and compared with the genetic algorithm in MATLAB. Our paper underlines the implementation of constrained differential evolution by varying two parameters, a predefined parameter for feasibility and the scaling factor, to ensure the convergence of the solution.

scholarly journals Information Theoretic Objective Function for Genetic Software Clustering

Proceedings ◽  
2019 ◽  
Vol 46 (1) ◽  
pp. 18
Author(s):  
Habib Izadkhah ◽  
Mahjoubeh Tajgardan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Objective Function ◽  
High Performance ◽  
Evolutionary Process ◽  
Program Comprehension ◽  
Software Systems ◽  
Objective Functions ◽  
Information Theoretic ◽  
Software Clustering

Software clustering is usually used for program comprehension. Since it is considered to be the most crucial NP-complete problem, several genetic algorithms have been proposed to solve this problem. In the literature, there exist some objective functions (i.e., fitness functions) which are used by genetic algorithms for clustering. These objective functions determine the quality of each clustering obtained in the evolutionary process of the genetic algorithm in terms of cohesion and coupling. The major drawbacks of these objective functions are the inability to (1) consider utility artifacts, and (2) to apply to another software graph such as artifact feature dependency graph. To overcome the existing objective functions’ limitations, this paper presents a new objective function. The new objective function is based on information theory, aiming to produce a clustering in which information loss is minimized. For applying the new proposed objective function, we have developed a genetic algorithm aiming to maximize the proposed objective function. The proposed genetic algorithm, named ILOF, has been compared to that of some other well-known genetic algorithms. The results obtained confirm the high performance of the proposed algorithm in solving nine software systems. The performance achieved is quite satisfactory and promising for the tested benchmarks.

A Species Conserving Genetic Algorithm for Multimodal Function Optimization

2002 ◽  
Vol 10 (3) ◽  
pp. 207-234 ◽  
Author(s):  
Jian-Ping Li ◽  
Marton E. Balazs ◽  
Geoffrey T. Parks ◽  
P. John Clarkson
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Multiple Solutions ◽  
Species Conservation ◽  
Function Optimization ◽  
Test Problems ◽  
Next Generation ◽  
Multimodal Function Optimization ◽  
Multimodal Function ◽  
A New Technique

This paper introduces a new technique called species conservation for evolving paral-lel subpopulations. The technique is based on the concept of dividing the population into several species according to their similarity. Each of these species is built around a dominating individual called the species seed. Species seeds found in the current gen-eration are saved (conserved) by moving them into the next generation. Our technique has proved to be very effective in finding multiple solutions of multimodal optimiza-tion problems. We demonstrate this by applying it to a set of test problems, including some problems known to be deceptive to genetic algorithms.

scholarly journals PROBABILISTIC ADAPTIVE CROSSOVER (PAX): A NOVEL GENETIC ALGORITHM CROSSOVER METHODOLOGY

2008 ◽  
Vol 17 (06) ◽  
pp. 1131-1160 ◽  
Author(s):  
SEBASTIÁN A. SALAH ◽  
MANUEL A. DUARTE-MERMOUD ◽  
NICOLÁS H. BELTRÁN
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Probability Distribution ◽  
Test Problems ◽  
Probability Vector ◽  
Uniform Crossover ◽  
Royal Road ◽  
Adaptive Crossover

A new crossover technique for genetic algorithms is proposed in this paper. The technique is called probabilistic adaptive crossover and denoted by PAX. The method includes the estimation of the probability distribution of the population, in order to store in a unique probability vector P information about the best and the worse solutions of the problem to be solved. The proposed methodology is compared with six crossover techniques namely: one-point crossover, two-point crossover, SANUX, discrete crossover, uniform crossover and selective crossover. These methodologies were simulated and compared over five test problems described by ONEMAX Function, Royal Road Function, Random L-MaxSAT, Bohachevsky Function, and the Himmelblau Function.

Application of Genetic Algorithms to the Synthesis of Riser Configurations

2003 ◽  
Author(s):  
Luciano T. Vieira ◽  
Beatriz de S. L. P. de Lima ◽  
Alexandre G. Evsukoff ◽  
Breno P. Jacob
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Optimization Techniques ◽  
Objective Functions ◽  
Random Choice ◽  
Large Space ◽  
Steel Catenary Riser ◽  
Deep Waters ◽  
Optimization Methodology

The purpose of this work is to describe the application of Genetic Algorithms in the search of the best configuration of catenary riser systems in deep waters. Particularly, an optimization methodology based on genetic algorithms is implemented on a computer program, in order to seek an optimum geometric configuration for a steel catenary riser in a lazy-wave configuration. This problem is characterized by a very large space of possible solutions; the use of traditional methods is an exhaustive work, since there is a large number of variables and parameters that define this type of system. Genetic algorithms are more robust than the more commonly used optimization techniques. They use random choice as a tool to guide a search toward regions of the search space with likely improvements. Some differences such as the coding of the parameter set, the search from a population of points, the use of objective functions and randomized operators are factors that contribute to the robustness of a genetic algorithm and result in advantages over traditional techniques. The implemented methodology has as baseline one or more criteria established by the experience of the offshore engineer. The implementation of an intelligent methodology oriented specifically to the optimization and synthesis of riser configurations will not only facilitate the work of manipulating a huge mass of data, but also assure the best alternative between all the possible ones, searching in a much larger space of possible solutions than classical methods.

THE ROLE OF MUTATION AND POPULATION SIZE IN GENETIC ALGORITHMS APPLIED TO PHYSICS PROBLEMS

2001 ◽  
Vol 12 (09) ◽  
pp. 1345-1355 ◽  
Author(s):  
E. BELMONT-MORENO
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Population Size ◽  
Mutation Rate ◽  
Test Problems ◽  
Optimum Performance ◽  
Analytical Expressions ◽  
Standard Genetic Algorithm ◽  
Physics Problems

A standard Genetic Algorithm is applied to a set of test problems, three of them taken from physics and the rest analytical expressions explicitly constructed to test search procedures. The relation between mutation rate and population size in the search for optimum performance is obtained showing similar behavior in these problems.

scholarly journals EXPERIMENTAL ANALYSIS OF MULTINATIONAL GENETIC ALGORITHM AND ITS MODIFICATIONS

2021 ◽  
pp. 71-83
Author(s):  
N. M. Gulayeva ◽  
S. A. Yaremko
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Experimental Analysis ◽  
Optimization Problems ◽  
Analysis Of Algorithms ◽  
Fitness Function ◽  
Performance Criteria ◽  
Test Problems ◽  
Test Functions ◽  
Multimodal Function

Context. Niching genetic algorithms are one of the most popular approaches to solve multimodal optimization problems. When classifying niching genetic algorithms it is possible to select algorithms explicitly analyzing topography of fitness function landscape; multinational genetic algorithm is one of the earliest examples of these algorithms. Objective. Development and analysis of the multinational genetic algorithm and its modifications to find all maxima of a multimodal function. Method. Experimental analysis of algorithms is carried out. Numerous runs of algorithms on well-known test problems are conducted and performance criteria are computed, namely, the percentage of convergence, real (global, local) and fake peak ratios; note that peak rations are computed only in case of algorithm convergence. Results. Software implementation of a multinational genetic algorithm has been developed and experimental tuning of its parameters has been carried out. Two modifications of hill-valley function used for determining the relative position of individuals have been proposed. Experimental analysis of the multinational genetic algorithm with classic hill-valley function and with its modifications has been carried out. Conclusions. The scientific novelty of the study is that hill-valley function modifications producing less number of wrong identifications of basins of attraction in comparison with classic hill-valley function are proposed. Using these modifications yields to performance improvements of the multinational genetic algorithm for a number of test functions; for other test functions improvement of the quality criteria is accompanied by the decrease of the convergence percentage. In general, the convergence percentage and the quality criterion values demonstrated by the algorithm studied are insufficient for practical use in comparison with other known algorithms. At the same time using modified hill-valley functions as a post-processing step for other niching algorithms seems to be a promising improvement of performance of these algorithms.

A Hybrid Fuzzy Simplex Genetic Algorithm

2004 ◽  
Vol 126 (6) ◽  
pp. 969-974 ◽  
Author(s):  
Mohamed B. Trabia
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Local Search ◽  
Local Minimum ◽  
Hybrid Genetic Algorithm ◽  
Simplex Algorithm ◽  
Standard Test ◽  
Test Problems ◽  
Design Problems ◽  
Engineering Design Problems

This paper presents a novel hybrid genetic algorithm that has the ability of the genetic algorithms to avoid being trapped at local minimum while accelerating the speed of local search by using the fuzzy simplex algorithm. The new algorithm is labeled the hybrid fuzzy simplex genetic algorithm (HFSGA). Standard test problems are used to evaluate the efficiency of the algorithm. The algorithm is also applied successfully to several engineering design problems. The HFSGA generally results in a faster convergence toward extremum.

scholarly journals Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems

1999 ◽  
Vol 7 (3) ◽  
pp. 205-230 ◽  
Author(s):  
Kalyanmoy Deb
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Test Problems ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Single Objective

In this paper, we study the problem features that may cause a multi-objective genetic algorithm (GA) difficulty in converging to the true Pareto-optimal front. Identification of such features helps us develop difficult test problems for multi-objective optimization. Multi-objective test problems are constructed from single-objective optimization problems, thereby allowing known difficult features of single-objective problems (such as multi-modality, isolation, or deception) to be directly transferred to the corresponding multi-objective problem. In addition, test problems having features specific to multi-objective optimization are also constructed. More importantly, these difficult test problems will enable researchers to test their algorithms for specific aspects of multi-objective optimization.

Formulation of Colors Using a Genetic Algorithm

2012 ◽  
Vol 17 (4) ◽  
pp. 241-244
Author(s):  
Cezary Draus ◽  
Grzegorz Nowak ◽  
Maciej Nowak ◽  
Marcin Tokarski
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Production Process ◽  
Computer Technology ◽  
Manual Method ◽  
Apply Genetic ◽  
Computer Aided ◽  
Multiple Samples ◽  
Plastics Industry

Abstract The possibility to obtain a desired color of the product and to ensure its repeatability in the production process is highly desired in many industries such as printing, automobile, dyeing, textile, cosmetics or plastics industry. So far, most companies have traditionally used the "manual" method, relying on intuition and experience of a colorist. However, the manual preparation of multiple samples and their correction can be very time consuming and expensive. The computer technology has allowed the development of software to support the process of matching colors. Nowadays, formulation of colors is done with appropriate equipment (colorimeters, spectrophotometers, computers) and dedicated software. Computer-aided formulation is much faster and cheaper than manual formulation, because fewer corrective iterations have to be carried out, to achieve the desired result. Moreover, the colors are analyzed with regard to the metamerism, and the best recipe can be chosen, according to the specific criteria (price, quantity, availability). Optimaization problem of color formulation can be solved in many diferent ways. Authors decided to apply genetic algorithms in this domain.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

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