A hybridization of clonal selection algorithm with iterated local search and variable neighborhood search for the feature selection problem

2015 ◽  
Vol 7 (3) ◽  
pp. 181-201 ◽  
Author(s):  
Magdalene Marinaki ◽  
Yannis Marinakis
Keyword(s):  
Feature Selection ◽  
Local Search ◽  
Variable Neighborhood Search ◽  
Clonal Selection ◽  
Iterated Local Search ◽  
Selection Problem ◽  
Neighborhood Search ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Feature Selection Problem

scholarly journals CAN LINEAR PROGRAMMING ASSIST METAHEURISTICS IN FOREST PRODUCTION PLANNING PROBLEM?

FLORESTA ◽  
2021 ◽  
Vol 51 (3) ◽  
pp. 751
Author(s):  
Carlos Alberto Araújo Júnior ◽  
Renato Vinícius Oliveira Castro ◽  
João Batista Mendes ◽  
Helio Garcia Leite
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Variable Neighborhood Search ◽  
Clonal Selection ◽  
Neighborhood Search ◽  
Initial Population ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Forest Production ◽  
Relaxed Problem

The planning of forest production requires the adoption of mathematical models to optimize the utilization of available resources. Hence, studies involving the improvement of decision-making processes must be performed. Herein, we evaluate an alternative method for improving the performance of metaheuristics when they are applied for identifying solutions to problems in forest production planning. The inclusion of a solution obtained by rounding the optimal solution of linear programming to a relaxed problem is investigated. Such a solution is included in the initial population of the clonal selection algorithm, genetic algorithm, simulated annealing, and variable neighborhood search metaheuristics when it is used to generate harvest and planting plans in an area measuring 4,210 ha comprising 120 management units with ages varying between 1 and 6 years. The same algorithms are executed without including the solutions mentioned in the initial population. Results show that the performance of the clonal selection algorithm, genetic algorithm, and variable neighborhood search algorithms improved significantly. Positive effects on the performance of the simulated annealing metaheuristic are not indicated. Hence, it is concluded that rounding off the solution to a relaxed problem is a good alternative for generating an initial solution for metaheuristics.

Extracting Information in Agricultural Data Using Fuzzy-Rough Sets Hybridization and Clonal Selection Theory Inspired Algorithms

2016 ◽  
Vol 30 (09) ◽  
pp. 1660008 ◽  
Author(s):  
Ayodele Lasisi ◽  
Rozaida Ghazali ◽  
Mustafa Mat Deris ◽  
Tutut Herawan ◽  
Fola Lasisi
Keyword(s):  
Feature Selection ◽  
Artificial Immune System ◽  
Clonal Selection ◽  
Recognition System ◽  
Clonal Selection Algorithm ◽  
Immune Recognition ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Detection Rates ◽  
Clonal Selection Theory

Mining agricultural data with artificial immune system (AIS) algorithms, particularly the clonal selection algorithm (CLONALG) and artificial immune recognition system (AIRS), form the bedrock of this paper. The fuzzy-rough feature selection (FRFS) and vaguely quantified rough set (VQRS) feature selection are coupled with CLONALG and AIRS for improved detection and computational efficiencies. Comparative simulations with sequential minimal optimization and multi-layer perceptron reveal that the CLONALG and AIRS produced significant results. Their respective FRFS and VQRS upgrades namely, FRFS-CLONALG, FRFS-AIRS, VQRS-CLONALG, and VQRS-AIRS, are able to generate the highest detection rates and lowest false alarm rates. Thus, gathering useful information with the AIS models can help to enhance productivity related to agriculture.

LAMARCKIAN LEARNING IN CLONAL SELECTION ALGORITHM FOR NUMERICAL OPTIMIZATION

2010 ◽  
Vol 19 (01) ◽  
pp. 19-37 ◽  
Author(s):  
MAOGUO GONG ◽  
LICHENG JIAO ◽  
JIE YANG ◽  
FANG LIU
Keyword(s):  
Local Search ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Clonal Selection ◽  
Benchmark Problems ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Tournament Selection ◽  
Selection Operator ◽  
Lamarckian Learning

In this paper, we introduce Lamarckian learning theory into the Clonal Selection Algorithm and propose a sort of Lamarckian Clonal Selection Algorithm, termed as LCSA. The major aim is to utilize effectively the information of each individual to reinforce the exploitation with the help of Lamarckian local search. Recombination operator and tournament selection operator are incorporated into LCSA to further enhance the ability of global exploration. We compare LCSA with the Clonal Selection Algorithm in solving twenty benchmark problems to evaluate the performance of LCSA. The results demonstrate that the Lamarckian local search makes LCSA more effective and efficient in solving numerical optimization problems.

Improved Immune Clonal Selection Algorithm and its Application in Power Network Planning

2012 ◽  
Vol 614-615 ◽  
pp. 1635-1640
Author(s):  
Qiong Liu ◽  
Tian Yang Li
Keyword(s):  
Local Search ◽  
Adaptive Algorithm ◽  
Clonal Selection ◽  
Network Planning ◽  
Global Search ◽  
Clonal Selection Algorithm ◽  
Power Network ◽  
Selection Algorithm ◽  
Adaptive Immune ◽  
Power Network Planning

Power network planning is a NP hard problem difficult to be solved. It can be contributed to similar TSP problem. Aiming at the slow convergence speed of the traditional immune clonal selection algorithm (ICA), adaptive immune clonal selection algorithm without memory（AICA）and adaptive immune clonal selection algorithm with memory(AICAM) are proposed respectively based on the combination of adaptive algorithm of clonal probability, immune probability , and group disaster algorithm. The two proposed algorithms have been applied to Power network planning problem. The adaptive algorithm has strong global search ability and weak local search ability at early evolution. Global search ability is weakened and local search ability is enhanced with the process of evolution in order to find global optimal point. The application of group disaster algorithm can enhance the diversity of the population and avoid the premature problems to some extent. Simulation results indicate that compared with the traditional immune clonal selection algorithm(ICA), the proposed algorithms can enhance the diversity of the population, avoid the premature problems, and can accelerate convergence speed in some extent.

Feature Selection Based on Clonal Selection Algorithm

2011 ◽  
pp. 180-203
Author(s):  
Xiangrong Zhang ◽  
Fang Liu
Keyword(s):  
Feature Selection ◽  
Optimization Problem ◽  
Clonal Selection ◽  
Good Choice ◽  
Affinity Maturation ◽  
Clonal Selection Algorithm ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Rapid Convergence ◽  
Optimal Feature Subset

The problem of feature selection is fundamental in various tasks like classification, data mining, image processing, conceptual learning, and so on. Feature selection is usually used to achieve the same or better performance using fewer features. It can be considered as an optimization problem and aims to find an optimal feature subset from the available features according to a certain criterion function. Clonal selection algorithm is a good choice in solving an optimization problem. It introduces the mechanisms of affinity maturation, clone, and memorization. Rapid convergence and good global searching capability characterize the performance of the corresponding operations. In this study, the property of rapid convergence to global optimum of clonal selection algorithm is made use of to speed up the searching of the most appropriate feature subset among a huge number of possible feature combinations. Compared with the traditional genetic algorithm-based feature selection, the clonal selection algorithm-based feature selection can find a better feature subset for classification. Experimental results on datasets from UCI learning repository, 16 types of Brodatz textures classification, and synthetic aperture radar (SAR) images classification demonstrated the effectiveness and good performance of the method in applications.

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