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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
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Nature-Inspired Chemical Reaction Optimisation Algorithms.

Nazmul Siddique1, Hojjat Adeli2

  • 1School of Computing and Intelligent Systems, University of Ulster, Northland Road, Londonderry, BT48 7JL UK.

Cognitive Computation
|August 29, 2017
PubMed
Summary
This summary is machine-generated.

Nature-inspired algorithms like Chemical Reaction Optimization (CRO) are key in machine learning. This review details CRO, its variants, and parameter selection for optimization problems.

Keywords:
Biologically inspired algorithmChemical reaction optimisationNature-inspired computingPhysics inspired algorithms

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Area of Science:

  • Computational intelligence
  • Meta-heuristic optimization
  • Machine learning algorithms

Background:

  • Nature-inspired meta-heuristic algorithms have been prominent in machine learning and cognitive computing for three decades.
  • Chemical Reaction Optimization (CRO) is a population-based meta-heuristic algorithm inspired by chemical reaction principles.
  • The CRO algorithm models the transformation of reactants into products to solve optimization problems.

Purpose of the Study:

  • To provide an overview of chemical reactions and their application to optimization problems.
  • To present a review of the Chemical Reaction Optimization (CRO) algorithm and its variants.
  • To summarize guidelines for effective CRO parameter selection in optimization.

Main Methods:

  • Overview of chemical reaction principles.
  • Review and synthesis of existing literature on CRO and its variations.
  • Summary of best practices for CRO parameter tuning.

Main Results:

  • Detailed explanation of how chemical reaction concepts are adapted for optimization.
  • Comprehensive review identifying diverse CRO variants.
  • Consolidated guidelines for selecting appropriate CRO parameters.

Conclusions:

  • Chemical Reaction Optimization (CRO) offers a robust approach for solving complex optimization problems.
  • Understanding CRO variants and parameter tuning is crucial for effective application.
  • This work provides a valuable resource for researchers and practitioners in machine learning and optimization.