Chromosome
A chromosome is an encoded representation of a candidate solution in an evolutionary algorithm, composed of genes or other solution components.
Advanced Algorithms and Data Structures
Chromosome encoding is the representation of a candidate solution as a chromosome composed of genes.
- How it is used
- Represents candidate solutions as genes that a genetic algorithm can initialize, recombine, mutate, and evaluate.
- How deep it goes
- Works through bit-string and permutation encodings, operator implementations, and constraint handling; leaves maximum-flow implementation to the reader.
- Examples
- Uses Mars-mission knapsack packing, traveling-salesperson tours, vertex cover, and maximum flow to contrast encodings.
- What you can do after
- Build it from scratch by choosing genes and adapting initialization, fitness, crossover, and mutation to the solution's constraints.
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Classic Computer Science Problems in Swift: Essential techniques for practicing programmers
A chromosome is a candidate solution representation that provides fitness evaluation, copying, random initialization, crossover, mutation, and human-readable output for a genetic algorithm.
- How it is used
- Represents candidate solutions that a genetic algorithm evaluates and evolves across generations.
- How deep it goes
- Defines a six-part Swift protocol and works through a puzzle encoding, while leaving chromosome design problem-specific.
- Examples
- Uses a SEND+MORE=MONEY letter-to-digit array and proposes a BitString implementation as an exercise.
- What you can do after
- Build it from scratch by implementing the Chromosome protocol for a candidate representation and its fitness, crossover, and mutation.
Optimization Algorithms: AI techniques for design, planning, and control problems
A chromosome is an encoded candidate solution in a genetic algorithm, with elements representing decision values or solution components.
- How it is used
- Represents candidate solutions that a genetic algorithm evaluates and evolves to optimize a ticket price.
- How deep it goes
- Defines encoding schemes and works through binary initialization, scoring, crossover, and mutation; names real-value and permutation encoding without comparable walkthroughs.
- Examples
- Uses eight-bit ticket-price candidates, an initial population with profit scores, and Python implementations using binary lists and pymoo.
- What you can do after
- Build it from scratch by initializing, decoding, scoring, and evolving binary chromosomes for the ticket-pricing example.
Evolutionary Deep Learning: Genetic algorithms and neural networks
A computational chromosome is an encoded segment of a candidate solution that groups genes for evolutionary variation and recombination.
- How it is used
- Represents candidate solutions as gene sequences that evolutionary operators can mutate and recombine.
- How deep it goes
- Defines chromosome structure and coding choices, then demonstrates multi-chromosome GEP configuration without developing biological or algorithmic theory deeply.
- Examples
- Uses binary or arbitrary-valued gene sequences, multi-chromosome GEP individuals, and an evolved equation simplified to 6*x1 + 22.
- What you can do after
- Apply it via Geppy to define chromosome structure, evolve expression-based solutions, and inspect simplified results.
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Grokking AI Algorithms, Second Edition
A chromosome is an encoded representation of a candidate solution in an evolutionary algorithm, consisting of gene or solution-value components.
- How it is used
- Represents candidate solutions so evolutionary algorithms can initialize, evaluate, recombine, and mutate populations.
- How deep it goes
- Defines genes, alleles, genotypes, and phenotypes, then works through binary encoding, validity, crossover, and mutation; defers other encodings.
- Examples
- Uses binary Knapsack chromosomes, a nine-gene word-selection chromosome, two-point crossover, and bit-string mutation.
- What you can do after
- Build it from scratch by encoding solutions, generating valid populations, and applying crossover or mutation to chromosome genes.
Read how Grokking AI Algorithms, Second Edition explains chromosome →
Related concepts
- genehas part · A gene is a discrete unit of encoded information representing a hereditary biological trait or a variable within an evolutionary candidate solution.
- crossoverused by · Crossover is an evolutionary recombination technique that combines material from parent candidate solutions to produce one or more offspring.
- fitnessmeasured by · Fitness is a numerical score indicating how well a candidate solution performs relative to an optimization objective.
- 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.
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