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Self-Adaptive Genetic Programming for Manufacturing Big Data Analysis. Symmetry (Basel) 2021. [DOI: 10.3390/sym13040709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
While black-box-based machine learning algorithms have high analytical consistency in manufacturing big data analysis, those algorithms experience difficulties in interpreting the results based on the manufacturing process principle. To overcome this limitation, we present a Self-Adaptive Genetic Programming (SAGP) for manufacturing big data analysis. In Genetic Programming (GP), the solution is expressed as a relationship between variables using mathematical symbols, and the solution with the highest explanatory power is finally selected. These advantages enable intuitive interpretation on manufacturing mechanisms and derive manufacturing principles based on the variables represented by formulas. However, GP occasionally has trouble adjusting the balance between high accuracy and detailed interpretation due to an incommensurable symmetry of the solutions. In order to effectively handle this drawback, we apply the self-adaptive mechanism into GP for managing crossover and mutation probabilities regarding the complexity of tree structure solutions in each generation. Our proposed algorithm showed equal or superior performance compared to other machine learning algorithms. We believe our proposed method can be applied in diverse manufacturing big data analytics in the future.
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Evaluation and Calibration of In Silico Models of Thrombin Generation Using Experimental Data from Healthy and Haemophilic Subjects. Bull Math Biol 2018; 80:1989-2025. [DOI: 10.1007/s11538-018-0440-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 04/20/2018] [Indexed: 01/17/2023]
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3
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Self-adaptation of learning rate in XCS working in noisy and dynamic environments. COMPUTERS IN HUMAN BEHAVIOR 2011. [DOI: 10.1016/j.chb.2010.10.024] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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4
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Goldstein M, Fredj E, Gerber RB. A new hybrid algorithm for finding the lowest minima of potential surfaces: Approach and application to peptides. J Comput Chem 2011; 32:1785-800. [DOI: 10.1002/jcc.21755] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2010] [Revised: 11/13/2010] [Accepted: 12/18/2010] [Indexed: 11/11/2022]
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Balamurugan R, Ramakrishnan C, Singh N. Performance evaluation of a two stage adaptive genetic algorithm (TSAGA) in structural topology optimization. Appl Soft Comput 2008. [DOI: 10.1016/j.asoc.2007.10.022] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Yang J, Wongsa S, Kadirkamanathan V, Billings SA, Wright PC. Metabolic flux estimation--a self-adaptive evolutionary algorithm with singular value decomposition. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2007; 4:126-38. [PMID: 17277420 DOI: 10.1109/tcbb.2007.1032] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Metabolic flux analysis is important for metabolic system regulation and intracellular pathway identification. A popular approach for intracellular flux estimation involves using 13C tracer experiments to label states that can be measured by nuclear magnetic resonance spectrometry or gas chromatography mass spectrometry. However, the bilinear balance equations derived from 13C tracer experiments and the noisy measurements require a nonlinear optimization approach to obtain the optimal solution. In this paper, the flux quantification problem is formulated as an error-minimization problem with equality and inequality constraints through the 13C balance and stoichiometric equations. The stoichiometric constraints are transformed to a null space by singular value decomposition. Self-adaptive evolutionary algorithms are then introduced for flux quantification. The performance of the evolutionary algorithm is compared with ordinary least squares estimation by the simulation of the central pentose phosphate pathway. The proposed algorithm is also applied to the central metabolism of Corynebacterium glutamicum under lysine-producing conditions. A comparison between the results from the proposed algorithm and data from the literature is given. The complexity of a metabolic system with bidirectional reactions is also investigated by analyzing the fluctuations in the flux estimates when available measurements are varied.
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Affiliation(s)
- Jing Yang
- Department of Automatic Control and Systems Engineering, University of Sheffield, UK.
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9
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Cheong F, Lai R. Simplifying the automatic design of a fuzzy logic controller using evolutionary programming. Soft comput 2006. [DOI: 10.1007/s00500-006-0135-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Fechner U, Schneider G. Flux (1): a virtual synthesis scheme for fragment-based de novo design. J Chem Inf Model 2006; 46:699-707. [PMID: 16563000 DOI: 10.1021/ci0503560] [Citation(s) in RCA: 61] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
It is demonstrated that the fragmentation of druglike molecules by applying simplistic pseudo-retrosynthesis results in a stock of chemically meaningful building blocks for de novo molecule generation. A stochastic search algorithm in conjunction with ligand-based similarity scoring (Flux: fragment-based ligand builder reaxions) facilitated the generation of new molecules using a single known reference compound as a template. This molecule assembly method is applicable in the absence of receptor-structure information. In a case study, we used imantinib (Gleevec) and a Factor Xa inhibitor as the reference structures. The algorithm succeeded in redesigning the templates from scratch and suggested several alternative molecular structures. The resulting designed molecules were chemically reasonable and contained essential substructure motifs. A comparison of molecular descriptors suggests that holographic descriptors might be advantageous over binary fingerprints for ligand-based de novo design.
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Affiliation(s)
- Uli Fechner
- Johann Wolfgang Goethe-Universität, Institut für Organische Chemie und Chemische Biologie, Marie-Curie-Str. 11, D-60439 Frankfurt, Germany
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A preliminary investigation into directed mutations in evolutionary algorithms. ACTA ACUST UNITED AC 2005. [DOI: 10.1007/3-540-61723-x_997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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12
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Inductive learning of mutation step-size in evolutionary parameter optimization. ACTA ACUST UNITED AC 2005. [DOI: 10.1007/bfb0014816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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Moles CG, Mendes P, Banga JR. Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res 2003; 13:2467-74. [PMID: 14559783 PMCID: PMC403766 DOI: 10.1101/gr.1262503] [Citation(s) in RCA: 622] [Impact Index Per Article: 29.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Here we address the problem of parameter estimation (inverse problem) of nonlinear dynamic biochemical pathways. This problem is stated as a nonlinear programming (NLP) problem subject to nonlinear differential-algebraic constraints. These problems are known to be frequently ill-conditioned and multimodal. Thus, traditional (gradient-based) local optimization methods fail to arrive at satisfactory solutions. To surmount this limitation, the use of several state-of-the-art deterministic and stochastic global optimization methods is explored. A case study considering the estimation of 36 parameters of a nonlinear biochemical dynamic model is taken as a benchmark. Only a certain type of stochastic algorithm, evolution strategies (ES), is able to solve this problem successfully. Although these stochastic methods cannot guarantee global optimality with certainty, their robustness, plus the fact that in inverse problems they have a known lower bound for the cost function, make them the best available candidates.
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Affiliation(s)
- Carmen G Moles
- Process Engineering Group, Instituto de Investigaciones Marinas (CSIC), 36208 Vigo, Spain
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He R, Narayana PA. Global optimization of mutual information: application to three-dimensional retrospective registration of magnetic resonance images. Comput Med Imaging Graph 2002; 26:277-92. [PMID: 12074923 DOI: 10.1016/s0895-6111(02)00019-8] [Citation(s) in RCA: 62] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
A global optimization technique for image registration, based on mutual information, that can be used in conjunction with a multi-resolution paradigm is described. This technique combines genetic algorithm in continuous space, which is a stochastic method and is very efficient in large search space, with dividing rectangle, which is a deterministic method that theoretically guarantees global optimization and is efficient in small search space. Calculations were performed for determining the optimum parameters for implementing this method. This technique was applied to register magnetic resonance images of brain. For comparison, the registration results using AIR, a commonly employed software package, are presented.
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Affiliation(s)
- Renjie He
- Department of Radiology, University of Texas at Houston Medical School, MSB 2.100, 6431 Fannin, Houston, TX 77030, USA
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Performance improvement of self-adaptive evolutionary methods with a dynamic lower bound. INFORM PROCESS LETT 2002. [DOI: 10.1016/s0020-0190(01)00290-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Abstract
Self-adaptation is a common method for learning online control parameters in an evolutionary algorithm. In one common implementation, each individual in the population is represented as a pair of vectors (x, sigma), where x is the candidate solution to an optimization problem scored in terms of f(x), and sigma is the so-called strategy parameter vector that influences how offspring will be created from the individual. Experimental evidence suggests that the elements of sigma can sometimes become too small to explore the given response surface adequately. The evolutionary search then stagnates, until the elements of sigma grow sufficiently large as a result of random variation. A potential solution to this deficiency associates multiple strategy parameter vectors with a single individual. A single strategy vector is active at any time and dictates how offspring will be generated. Experiments are conducted on four 10-dimensional benchmark functions where the number of strategy parameter vectors is varied over 1, 2, 3, 4, 5, 10, and 20. The results indicate advantages for using multiple strategy parameter vectors. Furthermore, the relationship between the mean best result after a fixed number of generations and the number of strategy parameter vectors can be determined reliably in each case.
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Affiliation(s)
- D B Fogel
- Natural Selection, Inc., 3333 N. Torrey Pines Ct., Suite 200, La Jolla, CA 92037, USA.
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Deb K, Beyer HG. Self-adaptive genetic algorithms with simulated binary crossover. EVOLUTIONARY COMPUTATION 2001; 9:197-221. [PMID: 11382356 DOI: 10.1162/106365601750190406] [Citation(s) in RCA: 66] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Self-adaptation is an essential feature of natural evolution. However, in the context of function optimization, self-adaptation features of evolutionary search algorithms have been explored mainly with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the self-adaptive feature of real-parameter genetic algorithms (GAs) using a simulated binary crossover (SBX) operator and without any mutation operator. The connection between the working of self-adaptive ESs and real-parameter GAs with the SBX operator is also discussed. Thereafter, the self-adaptive behavior of real-parameter GAs is demonstrated on a number of test problems commonly used in the ES literature. The remarkable similarity in the working principle of real-parameter GAs and self-adaptive ESs shown in this study suggests the need for emphasizing further studies on self-adaptive GAs.
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Affiliation(s)
- K Deb
- Kanpur Genetic Algorithms Laboratory, Department of Mechanical Engineering, Indian Institute of Technology Kanpur, PIN 208 016, India.
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Schneider G, Clément-Chomienne O, Hilfiger L, Schneider P, Kirsch S, Böhm HJ, Neidhart W. Virtual Screening for Bioactive Molecules by Evolutionary De Novo Design. Angew Chem Int Ed Engl 2000. [DOI: 10.1002/1521-3773(20001117)39:22<4130::aid-anie4130>3.0.co;2-e] [Citation(s) in RCA: 75] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Schneider G, Clément-Chomienne O, Hilfiger L, Schneider P, Kirsch S, Böhm HJ, Neidhart W. Evolutionäres De-novo-Design bioaktiver Moleküle: ein Ansatz zum virtuellen Screening. Angew Chem Int Ed Engl 2000. [DOI: 10.1002/1521-3757(20001117)112:22<4305::aid-ange4305>3.0.co;2-n] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Hart WE, Rosin C, Belew RK, Morris GM. Improved Evolutionary Hybrids for Flexible Ligand Docking in AutoDock. NONCONVEX OPTIMIZATION AND ITS APPLICATIONS 2000. [DOI: 10.1007/978-1-4757-3218-4_12] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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West RME, De Schutter E, Wilcox GL. Using Evolutionary Algorithms to Search for Control Parameters in a Nonlinear Partial Differential Equation. EVOLUTIONARY ALGORITHMS 1999. [DOI: 10.1007/978-1-4612-1542-4_3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Anastasio MA, Yoshida H, Nagel R, Nishikawa RM, Doi K. A genetic algorithm-based method for optimizing the performance of a computer-aided diagnosis scheme for detection of clustered microcalcifications in mammograms. Med Phys 1998; 25:1613-20. [PMID: 9775365 DOI: 10.1118/1.598341] [Citation(s) in RCA: 36] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Computer-aided diagnosis (CAD) schemes have the potential of substantially increasing diagnostic accuracy in mammography by providing the advantages of having a second reader. Our laboratory has developed a CAD scheme for detecting clustered microcalcifications in digital mammograms that is being tested clinically at the University of Chicago Hospitals. Our CAD scheme contains a large number of parameters such as filter weights, threshold levels, and region of interest (ROI) sizes. The choice of these parameter values determines the overall performance of the system and thus must be carefully set. Unfortunately, when the number of parameters becomes large, it is very difficult to obtain the optimal performance, especially when the values of the parameters are correlated with each other. In this study, we address the problem of identifying the optimal overall performance by developing an automated method for the determination of the parameter values that maximize the performance of a mammographic CAD scheme. Our method utilizes a genetic algorithm to search through the possible parameter values, and provides the set of parameters that minimize a cost function which measures the performance of the scheme. Using a database of 89 digitized mammograms, our method demonstrated that the sensitivity of our CAD scheme can be increased from 80% to 87% at a false positive rate of 1.0 per image. We estimate the average performance of our CAD scheme on unknown cases by performing jackknife tests; this was previously not feasible when the parameters of the CAD scheme were determined in a nonautomated manner.
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Affiliation(s)
- M A Anastasio
- Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, Illinois 60637, USA
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Tewari A, Narayan P. Novel staging tool for localized prostate cancer: a pilot study using genetic adaptive neural networks. J Urol 1998; 160:430-6. [PMID: 9679892 DOI: 10.1016/s0022-5347(01)62916-1] [Citation(s) in RCA: 53] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
PURPOSE An estimated $1.5 billion is spent annually for direct medical expenses and an additional $2.5 billion for indirect costs for the management of prostate cancer. Today there are several procedures for staging prostate cancer, including lymph node dissection. Despite these procedures, the accuracy of predicting extracapsular disease remains low (range 37 to 63, mean 45%). Use of multiple staging procedures adds significantly to the costs of managing prostate cancer. Recently artificial intelligence based neural networks have become available for medical applications. Unlike traditional statistical methods, these networks do not assume linearity or homogeneity of variance and, thus, they are more accurate for clinical data. We applied this concept to staging localized prostate cancer and devised an algorithm that can be used for prostate cancer staging. MATERIALS AND METHODS Our study comprised 1,200 men with clinically organ confined prostate cancer who underwent preoperative staging using serum prostate specific antigen, systematic biopsy and Gleason scoring before radical prostatectomy and lymphadenectomy. The performance of the neural network was validated for a subset of patients and network predictions were compared with actual pathological stage. Mean patient age was 62.9 years, mean serum prostate specific antigen 8.1 ng./ml. and mean biopsy Gleason 6. Of the patients 55% had organ confined disease, 27% positive margins, 8% seminal vesicle involvement and 7% lymph node disease. Of margin positive patients 30% also had seminal vesicle involvement, while of seminal vesicle positive patients 50% also had positive margins. RESULTS The sensitivity of the network was 81 to 100%, and specificity was 72 to 75% for various predictions of margin, seminal vesicle and lymph node involvement. The negative predictive values tended to be relatively high for all 3 features (range 92 to 100%). The neural network missed only 8% of patients with margin positive disease, and 2% with lymph node and 0% with seminal vesicle involvement. CONCLUSIONS Our study suggests that neural networks may be useful as an initial staging tool for detection of extracapsular extension in patients with clinically organ confined prostate cancer. These networks preclude unnecessary staging tests for 63% of patients with clinically organ confined prostate cancer.
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Affiliation(s)
- A Tewari
- University of Florida and Department of Veterans Affairs Medical Center, Gainesville, USA
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Westhead DR, Clark DE, Murray CW. A comparison of heuristic search algorithms for molecular docking. J Comput Aided Mol Des 1997; 11:209-28. [PMID: 9263849 DOI: 10.1023/a:1007934310264] [Citation(s) in RCA: 97] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
This paper describes the implementation and comparison of four heuristic search algorithms (genetic algorithm, evolutionary programming, simulated annealing and tabu search) and a random search procedure for flexible molecular docking. To our knowledge, this is the first application of the tabu search algorithm in this area. The algorithms are compared using a recently described fast molecular recognition potential function and a diverse set of five protein-ligand systems. Statistical analysis of the results indicates that overall the genetic algorithm performs best in terms of the median energy of the solutions located. However, tabu search shows a better performance in terms of locating solutions close to the crystallographic ligand conformation. These results suggest that a hybrid search algorithm may give superior results to any of the algorithms alone.
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Affiliation(s)
- D R Westhead
- Proteus Molecular Design Ltd., Macclesfield, Cheshire, U.K
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Angeline PJ. Tracking extrema in dynamic environments. EVOLUTIONARY PROGRAMMING VI 1997. [DOI: 10.1007/bfb0014823] [Citation(s) in RCA: 67] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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Clark DE, Westhead DR. Evolutionary algorithms in computer-aided molecular design. J Comput Aided Mol Des 1996; 10:337-58. [PMID: 8877705 DOI: 10.1007/bf00124503] [Citation(s) in RCA: 75] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
In recent years, search and optimisation algorithms inspired by evolutionary processes have been applied with marked success to a wide variety of problems in diverse fields of study. In this review, we survey the growing application of these 'evolutionary algorithms' in one such area: computer-aided molecular design. In the course of the review, we seek to summarise the work to date and to indicate where evolutionary algorithms have met with success and where they have not fared so well. In addition to this, we also attempt to discern some future trends in both the basic research concerning these algorithms and their application to the elucidation, design and modelling of chemical and biochemical structures.
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Affiliation(s)
- D E Clark
- Proteus Molecular Design Ltd., Macclesfield, U.K
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