scholarly journals An Island Grouping Genetic Algorithm for Fuzzy Partitioning Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
S. Salcedo-Sanz ◽  
J. Del Ser ◽  
Z. W. Geem
Keyword(s):  
Fuzzy Clustering ◽  
Fitness Function ◽  
Distance Measures ◽  
Mutation Operators ◽  
Fuzzy Partitioning ◽  
Grouping Genetic Algorithm ◽  
Partitioning Problems ◽  
Overall Performance ◽  
Clustering Problems ◽  
Crossover And Mutation

This paper presents a novel fuzzy clustering technique based on grouping genetic algorithms (GGAs), which are a class of evolutionary algorithms especially modified to tackle grouping problems. Our approach hinges on a GGA devised for fuzzy clustering by means of a novel encoding of individuals (containing elements and clusters sections), a new fitness function (a superior modification of the Davies Bouldin index), specially tailored crossover and mutation operators, and the use of a scheme based on a local search and a parallelization process, inspired from an island-based model of evolution. The overall performance of our approach has been assessed over a number of synthetic and real fuzzy clustering problems with different objective functions and distance measures, from which it is concluded that the proposed approach shows excellent performance in all cases.

Evolutionary Nonlinear Multimodel Partitioning Filters

2001 ◽  
Vol 5 (1) ◽  
pp. 8-14 ◽  
Author(s):  
G. N. Befigiannis ◽  
◽  
E. N. Demiris ◽  
S. D. Likothanassis ◽  
◽  
...  
Keyword(s):  
Adaptive Filters ◽  
Initial Conditions ◽  
Fitness Function ◽  
Specific Model ◽  
Posteriori Probability ◽  
Extended Kalman Filters ◽  
A Posteriori Probability ◽  
Mutation Operators ◽  
Conditional Models ◽  
Crossover And Mutation

The problem of designing adaptive filters for nonlinear systems is faced in this work. The proposed evolution program combines the effectiveness of multimodel adaptive filters and the robustness of genetic algorithms (GAs). Specifically, a bank of different extended Kalman filters is implemented. Then, the a posteriori probability that a specific model of the bank of conditional models is the true one can be used as a GA fitness function. The superiority of the algorithm is that it evolves concurrently the models’ population with initial conditions. Thus, this procedure alleviates extended Kalman filter sensitivity in initial conditions, by estimating the best values. In addition to this, adaptive implementation is proposed that relieves the disadvantage of time-consuming GA implementation. Finally, a variety of defined crossover and mutation operators is investigated in order to accelerate the algorithm’s convergence.

scholarly journals Design and Application of Model Based on Bayesian Network

2021 ◽  
Vol 2074 (1) ◽  
pp. 012037
Author(s):  
Ying Shi
Keyword(s):  
Bayesian Network ◽  
Fitness Function ◽  
Parameter Learning ◽  
Error Feedback ◽  
Accuracy Rate ◽  
Network Learning ◽  
Knowledge Reasoning ◽  
Mutation Operators ◽  
Coding Method ◽  
Crossover And Mutation

Abstract At present, Bayesian networks lack consistent algorithms that support structure establishment, parameter learning, and knowledge reasoning, making it impossible to connect knowledge establishment and application processes. In view of this situation, by designing a genetic algorithm coding method suitable for Bayesian network learning, crossover and mutation operators with adjustment strategies, the fitness function for reasoning error feedback can be carried out. Experimental results show that the new algorithm not only simultaneously optimizes the network structure and parameters, but also can adaptively learn and correct inference errors, and has a more satisfactory knowledge inference accuracy rate.

Finding Community of Brain Networks Based on Neighbor Index and DPSO with Dynamic Crossover

2020 ◽  
Vol 15 (4) ◽  
pp. 287-299
Author(s):  
Jie Zhang ◽  
Junhong Feng ◽  
Fang-Xiang Wu
Keyword(s):  
Brain Function ◽  
Brain Networks ◽  
Discrete Particle Swarm Optimization ◽  
Mri Brain ◽  
Mutation Operators ◽  
Neural Unit ◽  
Crossover And Mutation ◽  
The Brain ◽  
Index Table ◽  
Dynamic Crossover

Background: : The brain networks can provide us an effective way to analyze brain function and brain disease detection. In brain networks, there exist some import neural unit modules, which contain meaningful biological insights. Objective:: Therefore, we need to find the optimal neural unit modules effectively and efficiently. Method:: In this study, we propose a novel algorithm to find community modules of brain networks by combining Neighbor Index and Discrete Particle Swarm Optimization (DPSO) with dynamic crossover, abbreviated as NIDPSO. The differences between this study and the existing ones lie in that NIDPSO is proposed first to find community modules of brain networks, and dose not need to predefine and preestimate the number of communities in advance. Results: : We generate a neighbor index table to alleviate and eliminate ineffective searches and design a novel coding by which we can determine the community without computing the distances amongst vertices in brain networks. Furthermore, dynamic crossover and mutation operators are designed to modify NIDPSO so as to alleviate the drawback of premature convergence in DPSO. Conclusion: The numerical results performing on several resting-state functional MRI brain networks demonstrate that NIDPSO outperforms or is comparable with other competing methods in terms of modularity, coverage and conductance metrics.

TABU PROGRAMMING: A NEW PROBLEM SOLVER THROUGH ADAPTIVE MEMORY PROGRAMMING OVER TREE DATA STRUCTURES

2011 ◽  
Vol 10 (02) ◽  
pp. 373-406 ◽  
Author(s):  
ABDEL-RAHMAN HEDAR ◽  
EMAD MABROUK ◽  
MASAO FUKUSHIMA
Keyword(s):  
Search Algorithm ◽  
Benchmark Problems ◽  
Problem Solver ◽  
Learning Tools ◽  
Mutation Operators ◽  
Tree Data ◽  
Tree Data Structure ◽  
Comprehensive Framework ◽  
Rate Of Success ◽  
Crossover And Mutation

Since the first appearance of the Genetic Programming (GP) algorithm, extensive theoretical and application studies on it have been conducted. Nowadays, the GP algorithm is considered one of the most important tools in Artificial Intelligence (AI). Nevertheless, several questions have been raised about the complexity of the GP algorithm and the disruption effect of the crossover and mutation operators. In this paper, the Tabu Programming (TP) algorithm is proposed to employ the search strategy of the classical Tabu Search algorithm with the tree data structure. Moreover, the TP algorithm exploits a set of local search procedures over a tree space in order to mitigate the drawbacks of the crossover and mutation operators. Extensive numerical experiments are performed to study the performance of the proposed algorithm for a set of benchmark problems. The results of those experiments show that the TP algorithm compares favorably to recent versions of the GP algorithm in terms of computational efforts and the rate of success. Finally, we present a comprehensive framework called Meta-Heuristics Programming (MHP) as general machine learning tools.

Simulation of the Pressure-Bearing Capacity Test Model in Architectural Decoration Design

2014 ◽  
Vol 716-717 ◽  
pp. 391-394
Author(s):  
Li Mei Guo ◽  
Ai Min Xiao
Keyword(s):  
Genetic Algorithm ◽  
Bearing Capacity ◽  
Test Method ◽  
Test Model ◽  
Improved Genetic Algorithm ◽  
Good Convergence ◽  
Mutation Operators ◽  
Process Pressure ◽  
Calculation Process ◽  
Crossover And Mutation

in architectural decoration process, pressure-bearing capacity test is the foundation of design, and is very important. To this end, a pressure-bearing capacity test method in architectural decoration design is proposed based on improved genetic algorithm. The selection, crossover and mutation operators in genetic algorithm are improved respectively. Using its fast convergence characteristics eliminate the pressure movement in the calculation process. The abnormal area of pressure-bearing existed in buildings which can ensure to be tested is added, to obtain accurate distribution information of the abnormal area of pressure-bearing. Simulation results show that the improved genetic algorithm has good convergence, can accurately test the pressure-bearing capacity in architectural decoration.

Solar Power Plant Optimization

2015 ◽  
pp. 274-299
Author(s):  
Carlos Sanchez Reinoso ◽  
Román Buitrago ◽  
Diego Milone
Keyword(s):  
Power Plant ◽  
Evolutionary Computation ◽  
Solar Power ◽  
Fitness Function ◽  
Solar Power Plant ◽  
Computation Techniques ◽  
Photovoltaic Power ◽  
Plant Optimization ◽  
Optimization Methodology ◽  
Crossover And Mutation

The objective of this chapter is to optimize the photovoltaic power plant considering the effects of variable shading on time and weather. For that purpose, an optimization scheme based on the simulator from Sanchez Reinoso, Milone, and Buitrago (2013) and on evolutionary computation techniques is proposed. Regarding the latter, the representation used and the proposed initialization mechanism are explained. Afterwards, the proposed algorithms that allow carrying out crossover and mutation operations for the problem are detailed. In addition, the designed fitness function is presented. Lastly, experiments are conducted with the proposed optimization methodology and the results obtained are discussed.

Knowledge-Intensive Evolutionary Algorithms for Solving a Healthcare Fleet Optimization Problem

2018 ◽  
pp. 192-223
Author(s):  
Carlos Adrian Catania ◽  
Cecilia Zanni-Merk ◽  
François de Bertrand de Beuvron ◽  
Pierre Collet
Keyword(s):  
Evolutionary Algorithms ◽  
Knowledge Engineering ◽  
Domain Ontology ◽  
Real Problem ◽  
Fitness Functions ◽  
Evaluation Functions ◽  
Mutation Operators ◽  
Knowledge Intensive ◽  
Definition Of ◽  
Crossover And Mutation

In this chapter, the authors show how knowledge engineering techniques can be used to guide the definition of evolutionary algorithms (EA) for problems involving a large amount of structured data, through the resolution of a real problem. Various representations of the fitness functions, the genome, and mutation/crossover operators adapted to different types of problems (routing, scheduling, etc.) have been proposed in the literature. However, real problems including specific constraints (legal restrictions, specific usages, etc.) are often overlooked by the proposed generic models. To ensure that these constraints are effectively considered, the authors propose a methodology based on the structuring of the conceptual model underlying the problem, as a labelled domain ontology suitable for optimization by EA. The authors show that a precise definition of the knowledge model with a labelled domain ontology can be used to describe the chromosome, the evaluation functions, and the crossover and mutation operators. The authors show the details for a real implementation and some experimental results.

scholarly journals Choosing Mutation and Crossover Ratios for Genetic Algorithms—A Review with a New Dynamic Approach

Information ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 390 ◽  
Author(s):  
Ahmad Hassanat ◽  
Khalid Almohammadi ◽  
Esra’a Alkafaween ◽  
Eman Abunawas ◽  
Awni Hammouri ◽  
...  
Keyword(s):  
Population Size ◽  
Optimization Problems ◽  
Large Population ◽  
Small Population ◽  
Mutation Rates ◽  
Mutation Operators ◽  
Tuning Methods ◽  
Two Parameters ◽  
The Impact ◽  
Crossover And Mutation

Genetic algorithm (GA) is an artificial intelligence search method that uses the process of evolution and natural selection theory and is under the umbrella of evolutionary computing algorithm. It is an efficient tool for solving optimization problems. Integration among (GA) parameters is vital for successful (GA) search. Such parameters include mutation and crossover rates in addition to population that are important issues in (GA). However, each operator of GA has a special and different influence. The impact of these factors is influenced by their probabilities; it is difficult to predefine specific ratios for each parameter, particularly, mutation and crossover operators. This paper reviews various methods for choosing mutation and crossover ratios in GAs. Next, we define new deterministic control approaches for crossover and mutation rates, namely Dynamic Decreasing of high mutation ratio/dynamic increasing of low crossover ratio (DHM/ILC), and Dynamic Increasing of Low Mutation/Dynamic Decreasing of High Crossover (ILM/DHC). The dynamic nature of the proposed methods allows the ratios of both crossover and mutation operators to be changed linearly during the search progress, where (DHM/ILC) starts with 100% ratio for mutations, and 0% for crossovers. Both mutation and crossover ratios start to decrease and increase, respectively. By the end of the search process, the ratios will be 0% for mutations and 100% for crossovers. (ILM/DHC) worked the same but the other way around. The proposed approach was compared with two parameters tuning methods (predefined), namely fifty-fifty crossover/mutation ratios, and the most common approach that uses static ratios such as (0.03) mutation rates and (0.9) crossover rates. The experiments were conducted on ten Traveling Salesman Problems (TSP). The experiments showed the effectiveness of the proposed (DHM/ILC) when dealing with small population size, while the proposed (ILM/DHC) was found to be more effective when using large population size. In fact, both proposed dynamic methods outperformed the predefined methods compared in most cases tested.

scholarly journals Genetic Algorithm: Reviews, Implementations, and Applications

2020 ◽  
Vol 10 (6) ◽  
pp. 57
Author(s):  
Tanweer Alam ◽  
Shamimul Qamar ◽  
Amit Dixit ◽  
Mohamed Benaida
Keyword(s):  
Genetic Algorithm ◽  
Natural Selection ◽  
Behavioral Science ◽  
Random Search ◽  
Statistical Investigation ◽  
Adaptive Technology ◽  
Mutation Operators ◽  
Engineering Pedagogy ◽  
Crossover And Mutation ◽  
Investigation Process

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as adaptive technology to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.

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