Evolutionary computation
Evolutionary computation is a family of search and optimization methods that iteratively improve candidate solutions through variation and selection inspired by biological evolution.
Evolutionary Deep Learning: Genetic algorithms and neural networks
An evolutionary optimizer is an optimization algorithm that searches candidate configurations through population-based variation and selection.
- How it is used
- Automates and optimizes deep-learning development across data, validation, hyperparameters, model options, and architectures.
- How deep it goes
- Defines populations and fitness, then develops simulations, differential evolution, and DEAP-based particle-search implementations without formal proofs.
- Examples
- Uses Conway’s Game of Life, continuous and discontinuous function approximation, CNN layer experiments, and DEAP particle-based hyperparameter tuning.
- What you can do after
- Apply it via DEAP to encode, evaluate, and iteratively improve deep-learning hyperparameter configurations.
Optimization Algorithms: AI techniques for design, planning, and control problems
Evolutionary computation is a family of computational methods inspired by biological evolution that iteratively improves candidate solutions through variation and selection.
- How it is used
- Uses populations of candidate solutions to seek optimal or near-optimal solutions through evolution-inspired search.
- How deep it goes
- Defines the population, fitness, selection, crossover, and mutation workflow, with tuning considerations rather than a full implementation of every EC variant.
- Examples
- Illustrates selection with a treasure-hunting team and compares initial-population sampling strategies using Python examples and a table.
- What you can do after
- Evaluate/understand evolutionary search components and compare population-initialization choices before experimenting with a genetic algorithm.
Grokking AI Algorithms, Second Edition
A population-based algorithmic technique that iteratively improves candidate solutions through variation and selection inspired by biological evolution.
- How it is used
- Uses population-based search to find strong solutions in large optimization spaces with many valid choices.
- How deep it goes
- Explains variation, selection, and repeated generations, while naming genetic-algorithm techniques without detailing their implementation in the supplied evidence.
- Examples
- Illustrates adaptation with peppered moths and optimization with an eight-item, 9 kg knapsack.
- What you can do after
- Evaluate/understand it by recognizing suitable optimization problems and weighing population-based exploration against computational expense.
Read how Grokking AI Algorithms, Second Edition explains evolutionary computation →
Related concepts
- genetic algorithmincludes · 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.
- evolutionuses · Evolution is the cumulative change in the characteristics, structure, or behavior of a population or system across successive generations or states.
- artificial intelligencekind of · Artificial intelligence is the field concerned with creating computational systems that perform tasks associated with human intelligence, such as learning, reasoning, perception, or decision-making.
- particle swarm optimizationincludes · Particle swarm optimization is a population-based optimization method in which candidate solutions adjust their positions using individual and collective search information.
- life simulationincludes · Life simulation is a computational approach that models observed natural life processes such as movement, interaction, growth, and death.
- binary-coded genetic algorithmincludes · A binary-coded genetic algorithm is a genetic algorithm whose candidate solutions use binary encoding.
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