Chromosome

Q: How deeply does Evolutionary Deep Learning: Genetic algorithms and neural networks cover chromosome?

Defines chromosome structure and coding choices, then demonstrates multi-chromosome GEP configuration without developing biological or algorithmic theory deeply.

As explained in 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.

A computational chromosome is a sequence or container of genes encoding an individual in a genetic algorithm. It groups traits so evolutionary operations can vary and recombine candidate solutions. Most implementations use one chromosome, although multiple chromosomes are possible. section 2.5.1 section 2.5.2

Model a chromosome by deciding:

Choose gene values such as numbers or Boolean 0/1 values; genes may also encode text, colors, or other trait information. section 2.5.2
Map a gene to one array value or several values, depending on the representation required by the solution. section 2.5.2
Apply crossover to exchange chromosome material and mutation to alter encoded traits during evolution. section 2.5.1 section 12.1
Split complex gene sequences into chromosomes when useful sections should remain isolated and swappable. section 12.1

A chromosome in Geppy

A gene-expression-programming setup creates genes with head length h = 7, places n_genes = 2 genes in each individual, and links them with addition. A population of 100 individuals then evolves for 100 generations; the best chromosome simplifies to the expression 6*x1 + 22.

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

Cover of Evolutionary Deep Learning: Genetic algorithms and neural networks

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Also explained in

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Advanced Algorithms and Data StructuresChromosome encoding is the representation of a candidate solution as a chromosome composed of genes.
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Classic Computer Science Problems in Swift: Essential techniques for practicing programmersA chromosome is a candidate solution representation that provides fitness evaluation, copying, random initialization, crossover, mutation, and human-readable output for a genetic algorithm.
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Optimization Algorithms: AI techniques for design, planning, and control problemsA chromosome is an encoded candidate solution in a genetic algorithm, with elements representing decision values or solution components.
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Grokking AI Algorithms, Second EditionA chromosome is an encoded representation of a candidate solution in an evolutionary algorithm, consisting of gene or solution-value components.

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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.

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.