Genetic Algorithm Performance with Different Selection Methods in Solving Multi-Objective Network Design Problem

Oladele, R. O.; Sadiku, J. S.

doi:10.5120/12012-7848

Cited by 12 publications

(10 citation statements)

References 7 publications

(7 reference statements)

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“…Some of tournament selection advantage includes proper time utilization mostly when the implementation is parallel with low susceptibility to being taken by dominant individuals, and no need for fitness scaling or sorting. [34].…”

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confidence: 99%

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

et al. 2019

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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.

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confidence: 99%

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

et al. 2019

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“…Kuo et al (2011) proposed Particle swarm optimization (PSO) based ARM algorithm (ARM-PSO) in an application of stock market to gauge speculation conduct and stock class buying. Oladele and Sadiku (2013) proposed a hybrid Genetic Algorithm for multitarget outline tricky abusing divergent parent decision. Martínez-Ballesteros et al (2015) enhanced the versatility of quantitative association rule mining systems in light of genetic calculations.…”

Section: Related Workmentioning

confidence: 99%

“…One of the most effective strategies for this problem is integrating optimization techniques with association rule mining for increasing its performance. Among the proposed algorithms are ARM-PSO (Kuo, Chao & Chiu, 2011), GA (Oladele & Sadiku, 2013), Cuckoo Search (Yun, Kim, Ryang, Lee & Lee, 2016;Mlakar et al, 2017), WFIM (Wensheng et al, 2017), HUIM-MMU (Jerry et al, 2016), CP-Miner (Thanh-Long, Bay & Vaclav, 2017), dynamic superset bit-vector (Tahrima et al, 2017) and lattice (Thang, Bay & Loan, 2017). However, most of the relevant algorithms for controlling large number of association rules are often computationally expensive and possibly generate much irrelevant rules.…”

Section: Introductionmentioning

confidence: 99%

ARM–AMO: An efficient association rule mining algorithm based on animal migration optimization

Sơn

Chiclana

Kumar³

et al. 2018

Knowledge-Based Systems

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“…O. Oladele, J. S. Sadiku [18] Selection is one of the key operations of genetic algorithm (GA). This paper presents a comparative analysis of GA performance in solving multiobjective network design problem (MONDP) using different parent selection methods.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Association Rule mining using Genetic Algorithm

Kanani¹,

Mishra²

2015

IJCA

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Association Rule Mining technique that attempt to unearthing interesting pattern or relationship between data in large Database. Genetic Algorithm is a search heuristic which is used to generate useful solution for optimization and search problems. Genetic Algorithm based evaluation in Mining Technique is backbone for mining interesting Rule based on GA parameters like fitness function, Crossover Rate, Mutation Rate. The key focus of this synthesize approach is to optimize the rule that generated by mining methodology and to provide more accurate results. The Proposed Approach is to generate rules based on Quantitative dataset, using the concept of threshold -frequent item sets are define as initial population which the first step of Genetic algorithm. Crossover & mutation is applied to generate more combination of rule & can identify Co-occurrence of item sets.

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Genetic Algorithm Performance with Different Selection Methods in Solving Multi-Objective Network Design Problem

Cited by 12 publications

References 7 publications

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

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

ARM–AMO: An efficient association rule mining algorithm based on animal migration optimization

An Optimized Association Rule mining using Genetic Algorithm

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