A new genetic algorithm for global optimization of multimodal continuous functions

Thakur, Manoj

doi:10.1016/j.jocs.2013.05.005

Cited by 60 publications

(27 citation statements)

References 25 publications

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“…This problem is solved by MPAGA which can improve the solution quality when we solve the high‐dimensional problems. In this paper, MPAGA introduces the following four concepts on simple GA An AGA with multipopulation is developed to speed up the computation by binding the power of parallel computers. The parallel execution of the population utilizes the multipopulation method for FS.…”

Section: Proposed Egsmentioning

confidence: 99%

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Identification of cancerous gene groups from microarray data by employing adaptive genetic and support vector machine technique

Shukla¹

2019

Computational Intelligence

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Nowadays, microarray gene expression data plays a vital role in tumor classification. However, due to the accessibility of a limited number of tissues compared to large number of genes in genomic data, various existing methods have failed to identify a small subset of discriminative genes. To overcome this limitation, in this paper, we developed a new hybrid technique for gene selection, called ensemble multipopulation adaptive genetic algorithm (EMPAGA) that can overlook the irrelevant genes and classify cancer accurately. The proposed hybrid gene selection algorithm comprises of two phase. In the first phase, an ensemble gene selection (EGS) method used to filter the noisy and redundant genes in high‐dimensional datasets by combining multilayer and F‐score approaches. Then, an adaptive genetic algorithm based on multipopulation strategy with support vector machine and naïve Bayes (NB) classifiers as a fitness function is applied for gene selection to select the extremely sensible genes from the reduced datasets. The performance of the proposed method is estimated on 10 microarray datasets of numerous tumor. The comprehensive results and various comparisons disclose that EGS has a remarkable impact on the efficacy of the adaptive genetic algorithm with multipopulation strategy and enhance the capability of the proposed approach in terms of convergence rate and solution quality. The experiments results demonstrate the superiority of the proposed method when compared to other standard wrappers regarding classification accuracy and optimal number of genes.

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Section: Proposed Egsmentioning

confidence: 99%

“…GA is a population-based stochastic optimization technique, initially introduced by John Holland. 38 It suffers from two noteworthy problems, that is, low convergence speed and falling in local optimal points. GA with adaptive parameters called AGA is the most popular and promising variant of GA.…”

Section: Adaptive Genetic Algorithmmentioning

confidence: 99%

Identification of cancerous gene groups from microarray data by employing adaptive genetic and support vector machine technique

Shukla¹

2019

Computational Intelligence

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“…GA falling under the category of evolutionary algorithms (Carter, 2003) searches for improved solutions based on the previous results. A sufficient number of iterations should be used to allow the algorithm for generating a number of attempts to find the optimum (Shopova and Vaklieva-Bancheva, 2006, Thakur, 2014, Toledo et al, 2014. The nature of GA is to generate parent-to-child sequences in parallel structures.…”

Section: Optimisation Algorithmsmentioning

confidence: 99%

Novel Integrated Approach Simultaneously Optimising AFI Locations Plus Number and (A)ICD Sizes

Dowlatabad¹

2015

All Days

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The optimal design of Advanced Well Completions (AWCs) is an important step in field development planning nowadays. However, determination of optimum design of (Autonomous) Inflow Control Devices [(A)ICDs] and their integration with Annular Flow Isolations (AFIs) in an advanced well completed across a heterogeneous reservoir is a challenge which has not been addressed properly yet. Most used, variable strength (A)ICD completion design, workflows by industry are based on trial and error evaluations of a static well-reservoir models. Several researchers also suggested relating the strengths of the devices to near wellbore static reservoir properties such as permeability of the encountering layers. Both of these approaches do not capture the dynamic interactions between the well and constantly changing reservoir conditions. Moreover, neglecting mutual dependency of AFI location and (A)ICD design in the traditionally step-by-step (A)ICD-AFI design workflows, may result in a design delivering low overall productivity throughout the field life.A novel, integrated (A)ICD-AFI completion design workflow is presented in this paper. The workflow simultaneously optimises (A)ICDs strengths and number plus locations of AFIs in the horizontal and/or multilateral wells based on field's production lifetime. The approach includes stochastic optimisation algorithm coupled with a reservoir simulator and an in-house completion design programming code. The code is developed to automatically generate various (A)ICD-AFI completion configurations required to determine the optimum completion design while honouring practical concerns and economics. A novel AFI modelling technique developed allows integration of AFIs in the optimisation process.The application of the new and the traditional workflows are illustrated using heterogeneous reservoir simulation models. A significant improvement in total oil production for the wells, designed by the new workflow, was achieved comparing with the wells designed by the traditional workflows. The study aims to help well engineers in designing optimal advanced well completion delivering maximum possible field's added-value.

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“…1) Laplace crossover (LX): this is a type of self-adaptive parent centric crossover operator that uses the Laplace distribution which is mathematically expressed as [25]:…”

Section: Crossover Operatorsmentioning

confidence: 99%

An empirical investigation into the effects of chaos on different types of evolutionary crossover operators for efficient global search in complicated landscapes

Emami

Mozaffari

Azad

et al. 2014

International Journal of Computer Mathematics

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In this study, a comprehensive empirical test is conducted to analyze the effects of two well-known chaotic maps, namely sinusoidal and logistic maps, on the efficacy of double Pareto crossover (DPX), Laplace crossover (LX) and simulated binary crossover (SBX) operators for the global optimization of continuous problems. To do so, 13 well-known numerical benchmark problems in three distinctive dimensions, namely 50D, 100D and 200D, are considered and the genetic algorithm (GA) with simple version and chaos enhanced versions of the mentioned crossover operators are utilized for optimizing these functions. Furthermore, a time complexity analysis is conducted to find out the impact of hybridizing the chaos and the evolutionary operators on the computational complexity of GA. The results of the experimental analysis provide us with fruitful information regarding the scalability, computational complexity and exploration/exploitation capability of the considered rival optimization algorithms, as well as, demonstrate the efficacy of chaos-evolutionary computing for numerical continuous optimizations.

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A new genetic algorithm for global optimization of multimodal continuous functions

Cited by 60 publications

References 25 publications

Identification of cancerous gene groups from microarray data by employing adaptive genetic and support vector machine technique

Identification of cancerous gene groups from microarray data by employing adaptive genetic and support vector machine technique

Novel Integrated Approach Simultaneously Optimising AFI Locations Plus Number and (A)ICD Sizes

An empirical investigation into the effects of chaos on different types of evolutionary crossover operators for efficient global search in complicated landscapes

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