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Related Concept Videos

Entropy02:39

Entropy

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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
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The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
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A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that...
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The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
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Chaotic genetic algorithm and the effects of entropy in performance optimization.

Guillermo Fuertes1, Manuel Vargas2, Miguel Alfaro3

  • 1Facultad de Ingeniería, Ciencia y Tecnología, Universidad Bernardo O'Higgins, Santiago 8370993, Chile.

Chaos (Woodbury, N.Y.)
|February 3, 2019
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Summary
This summary is machine-generated.

This study introduces a Chaotic Genetic Algorithm (CGA) that uses chaotic maps and analyzes population entropy. Higher entropy in the initial population directly correlates with improved performance in complex optimization tasks.

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Chaos theory applications

Background:

  • Genetic Algorithms (GAs) are powerful optimization tools but can struggle with complex search spaces.
  • Chaos theory offers methods to introduce non-linearity and improve stochastic processes.
  • The role of initial population entropy in GA performance is not fully understood.

Purpose of the Study:

  • To investigate the impact of initial population entropy on the performance of a Chaotic Genetic Algorithm (CGA).
  • To explore the use of chaotic maps for enhancing GA parameter initialization.
  • To establish a relationship between population entropy and optimization effectiveness in complex landscapes.

Main Methods:

  • Development of a Chaotic Genetic Algorithm (CGA) incorporating chaotic maps.
  • Modification of GA stochastic parameters using chaotic series for initial population generation.
  • Analysis of the entropy of the initial population.
  • Optimization of nine standard benchmark functions using eight distinct chaotic maps.

Main Results:

  • A direct relationship was observed between the entropy of the initial population and the algorithm's performance.
  • The use of chaotic maps effectively modified population parameters and influenced entropy levels.
  • Consistent performance improvements were noted across various benchmark functions with higher initial entropy.

Conclusions:

  • Initial population entropy is a critical factor for the success of the Chaotic Genetic Algorithm.
  • Chaotic maps provide a viable mechanism for tuning population entropy and enhancing optimization.
  • The CGA strategy demonstrates significant potential for tackling complex search space optimization problems.