Natural selection

As explained in Evolutionary Deep Learning: Genetic algorithms and neural networks

Natural selection is the biological process by which heritable traits better suited to an environment lead to greater survival or reproduction.

Natural selection describes how variation, survival, and reproduction favor traits that help organisms adapt to changing environments. Evolutionary computation turns this idea into a search process: simulate individuals, measure their fitness, and evolve better-performing solutions. section 2.4.3 section 1.1.1

A simplified computational model emphasizes:

• Representing one individual or a population of individuals inside an environment or problem space. section 1.1.1
• Quantifying performance with fitness, which determines how effectively an individual survives, thrives, or solves the problem. section 1.1.1
• Encouraging change and diversity rather than preserving only the current strongest individuals, because adaptation can accelerate problem solving. section 2.4.4

The biological analogy is deliberately simplified. Natural selection motivates evolutionary computation, but computational terminology does not transfer perfectly from biology. The practical goal is evolutionary change: producing solutions that respond well to changing conditions, not merely selecting the strongest current solution. section 2.4.4

Experiment with evolutionary operators

Construct gene sequences, then change the target function or alter crossover, selection, mutation, and other operators. Rerunning the experiment reveals how the available operators and their parameters affect evolution. section 4.1.2

Figure 1.1 Subset of EC used to apply EDL

Use the figure to locate natural selection within the subset of evolutionary computation applied to evolutionary deep learning. section 1.1.1

How Evolutionary Deep Learning: Genetic algorithms and neural networks covers it

How deep it goes

Explains fitness, population change, and diversity, while simplifying rather than developing biological genetics in detail.

Examples

Uses Darwin’s observations, gene-sequence construction, target functions, and experiments with crossover, selection, and mutation parameters.

What it assumes

Builds from zero through Darwin’s theory and connects its trait-inheritance context to later genetic explanations.

What you can do after

Apply it via evolutionary computation by defining fitness and experimenting with population diversity and evolutionary operators.

How it connects

Connects natural selection with evolutionary computation, fitness, genetics, evolutionary change, and evolutionary deep learning.

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Also explained in

• 

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  Advanced Algorithms and Data StructuresNatural selection is a process in which individuals with traits associated with greater survival or reproduction are more likely to contribute to subsequent generations.
• 

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  Grokking AI Algorithms, Second EditionNatural selection is the differential survival and reproduction of individuals according to how well their traits fit their environment.

Compare how 3 books explain natural selection →

Related concepts

• genetic algorithmused by · A genetic algorithm is an evolutionary optimization method that improves a population of candidate solutions through fitness-based selection and variation, often including recombination and mutation.
• evolutionproduces · Evolution is the cumulative change in the characteristics, structure, or behavior of a population or system across successive generations or states.
• fitnessmeasures · Fitness is a numerical score indicating how well a candidate solution performs relative to an optimization objective.
• evolutionary computationused by · Evolutionary computation is a family of search and optimization methods that iteratively improve candidate solutions through variation and selection inspired by biological evolution.
• intelligenceproduces · Intelligence is the capacity to acquire and apply knowledge, reason, learn, solve problems, and adapt to changing situations.

This explainer was generated by Manning's knowledge graph from the text of Evolutionary Deep Learning: Genetic algorithms and neural networks. The section links point to the parts of the book it draws on.