Evolutionary Algorithms: Concepts, Designs, and Applications in Bioinformatics: Evolutionary Algorithms for Bioinformatics

2015/08/03 by Ka‐Chun Wong, Ka-Chun Wong, Wong, Ka-Chun · 7 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Algorithm #Algorithms and Data Compression #Artificial intelligence #Bioinformatics #Biology #Computation (stat.CO) #Computer science #Evolutionary Algorithms and Applications #Evolutionary algorithm #Evolutionary computation #FOS: Biological sciences #FOS: Computer and information sciences #Genomics (q-bio.GN) #Metaheuristic Optimization Algorithms Research #Methodology (stat.ME) #Neural and Evolutionary Computing (cs.NE) #Quantitative Methods (q-bio.QM) #cs.NE #q-bio.GN #q-bio.QM #stat.CO #stat.ME

paper · pdf · doi:10.48550/arxiv.1508.00468

published in arXiv (Cornell University) (Cornell University)

arxiv created 2015/08/03 · openalex publication_date 2015/08/03 · arxiv updated 2015/08/04 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

Since genetic algorithm was proposed by John Holland (Holland J. H., 1975) in the early 1970s, the study of evolutionary algorithm has emerged as a popular research field (Civicioglu & Besdok, 2013). Researchers from various scientific and engineering disciplines have been digging into this field, exploring the unique power of evolutionary algorithms (Hadka & Reed, 2013). Many applications have been successfully proposed in the past twenty years. For example, mechanical design (Lampinen & Zelinka, 1999), electromagnetic optimization (Rahmat-Samii & Michielssen, 1999), environmental protection (Bertini, Felice, Moretti, & Pizzuti, 2010), finance (Larkin & Ryan, 2010), musical orchestration (Esling, Carpentier, & Agon, 2010), pipe routing (Furuholmen, Glette, Hovin, & Torresen, 2010), and nuclear reactor core design (Sacco, Henderson, Rios-Coelho, Ali, & Pereira, 2009). In particular, its function optimization capability was highlighted (Goldberg & Richardson, 1987) because of its high adaptability to different function landscapes, to which we cannot apply traditional optimization techniques (Wong, Leung, & Wong, 2009). Here we review the applications of evolutionary algorithms in bioinformatics.

Citations

• Analysis of the genome sequence of the flowering plant Arabidopsis thaliana
• Effect of Spatial Locality on an Evolutionary Algorithm for Multimodal Optimization
• DNAase footprinting a simple method for the detection of protein-DNA binding specificity
• Principles that Govern the Folding of Protein Chains
• A genetic algorithm tutorial
• Basic local alignment search tool
• An integrated map of genetic variation from 1,092 human genomes
• Ant colony system: a cooperative learning approach to the traveling salesman problem
• Mining association rules between sets of items in large databases
• An integrated encyclopedia of DNA elements in the human genome
• Finishing the euchromatic sequence of the human genome

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• Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges
• Qualities, challenges and future of genetic algorithms: a literature review