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Everything about Fitness function

Objective function of evolutionary algorithm

A fitness function is a particular type of objective or cost function that is used to summarize, as a single figure of merit, how close a given candidate solution is to achieving the set aims. It is an important component of evolutionary algorithms (EA), such as genetic programming, evolution strategies or genetic algorithms. An EA is a metaheuristic that reproduces the basic principles of biological evolution as a computer algorithm in order to solve challenging optimization or planning tasks, at least approximately. For this purpose, many candidate solutions are generated, which are evaluated using a fitness function in order to guide the evolutionary development towards the desired goal. Similar quality functions are also used in other metaheuristics, such as ant colony optimization or particle swarm optimization. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Fitness functionA 5-minute orientation
  2. Skim a recent paper: Association of physical activity and fitness with executive function among preschoolersInfluential work since 2022
  3. Pick up a classic book: The great ideas of clinical scienceThe most-reprinted book on the subject
  4. Read the landmark paper: Ageing, fitness and neurocognitive functionCited 1,054 times
  5. Go sideways: Multi-objective optimizationA closely related topic
  6. Go sideways: Pareto frontierA closely related topic

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🗂️ The subject at a glance

  1. Requirements of evaluation and fitness function
  2. Computational efficiency
  3. Multi-objective optimization
  4. Auxiliary objectives

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🆕 Influential since 2022

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