Natural selection
Natural selection is the evolutionary process by which heritable traits affecting survival or reproduction influence which variants become more common in a population.
Advanced Algorithms and Data Structures
Natural selection is a process in which individuals with traits associated with greater survival or reproduction are more likely to contribute to subsequent generations.
- How it is used
- Uses survival and reproduction as an optimization metaphor for transitioning a population toward better candidate solutions.
- How deep it goes
- Descends from biological analogy to a population-transition procedure with fitness evaluation, elitism, selection, crossover, and mutation.
- Examples
- Uses a Mars knapsack, calorie-per-weight food choices, and prey hiding strategies to illustrate fitness differences.
- What you can do after
- Apply it via genetic algorithms to build successive candidate populations from fitness, selection, crossover, mutation, and elitism.
Read how Advanced Algorithms and Data Structures explains natural selection →
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.
- How it is used
- Frames evolutionary computation as a practical search strategy for complex problems and machine-learning workflows.
- 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 you can do after
- Apply it via evolutionary computation by defining fitness and experimenting with population diversity and evolutionary operators.
Grokking AI Algorithms, Second Edition
Natural selection is the differential survival and reproduction of individuals according to how well their traits fit their environment.
- How it is used
- Frames evolutionary algorithms as computational searches modeled on variation, selection, reproduction, and repeated generations.
- How deep it goes
- Explains variation, environmental fit, survival, reproduction, and inheritance through a moth example, but stops short of formal population genetics.
- Examples
- Uses peppered moth camouflage and visual depictions of evolution, reproduction, and mutation.
- What you can do after
- Apply the mechanism as intuition for evolutionary algorithms that search large solution spaces through successive generations.
Read how Grokking AI Algorithms, Second Edition explains 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.
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