Gene
A gene is a discrete unit of encoded information representing a hereditary biological trait or a variable within an evolutionary candidate solution.
Advanced Algorithms and Data Structures
A gene is an individual position in a genetic-algorithm chromosome that represents one optimization variable and its assigned value.
- How it is used
- Encodes one optimization variable within a candidate solution’s chromosome.
- How deep it goes
- Develops binary and permutation encodings with mutation and crossover operations; sketches maximum-flow genes without implementing that algorithm.
- Examples
- Uses knapsack item bits, traveling-salesperson vertex permutations, and maximum-flow edge values.
- What you can do after
- Build it from scratch: choose gene domains and adapt mutation and crossover to preserve encoding constraints.
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Evolutionary Deep Learning: Genetic algorithms and neural networks
A gene is a heritable unit of biological information that influences an organism's traits and occupies a position on a chromosome.
- How it is used
- Connects inherited biological traits to the gene-and-chromosome representation used by genetic algorithms.
- How deep it goes
- Defines genes biologically and computationally, then demonstrates representations, but stops short of detailed molecular genetics.
- Examples
- Uses binary NumPy chromosomes and EvoLisa polygon genes containing coordinates, color, and transparency.
- What you can do after
- Apply genes by encoding traits as binary values, numeric arrays, or structured value groups for evolutionary optimization.
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Classic Computer Science Problems in Swift: Essential techniques for practicing programmers
Gene is a book-specific array-based representation of a nucleotide sequence as an ordered collection of codons.
- How it is used
- Represents nucleotide sequences for codon search and related algorithm exercises.
- How deep it goes
- Defines nucleotides, codons, array conversion, ordering, and searches without developing broader biological semantics.
- Examples
- Uses a nucleotide string, stringToGene(), an ACG codon, and linear or binary containment searches.
- What you can do after
- Build it from scratch as Swift enums, tuples, arrays, and a parser for codon sequences.
Grokking AI Algorithms, Second Edition
A gene is an individual value or position within an evolutionary algorithm chromosome that contributes to the encoded candidate solution.
- How it is used
- Represents one decision or value inside an evolutionary algorithm's encoded candidate solution.
- How deep it goes
- Explains gene structure, encoding, mutation, population creation, and fitness effects, while deferring nonbinary mechanisms to later treatment.
- Examples
- Uses binary Knapsack Problem chromosomes, a nine-gene word-selection exercise, mutation figures, and Python fitness and population code.
- What you can do after
- Build it from scratch by encoding decisions as genes, generating chromosomes, mutating values, and calculating fitness.
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Optimization Algorithms: AI techniques for design, planning, and control problems
A gene is a segment of genetic material that encodes a specific biological property or functional product.
- How it is used
- Uses genes as building blocks for biological traits and as encoded parameters in genetic-algorithm candidate solutions.
- How deep it goes
- Explains genes through alleles, loci, genotypes, phenotypes, and encoding, stopping short of molecular mechanisms.
- Examples
- Illustrates genes with appetite, movement, feet, and skin traits, then applies them to binary ticket-price chromosomes.
- What you can do after
- Evaluate/understand how biological genes map to genetic-algorithm parameters and chromosomes with suitable encodings.
Related concepts
- chromosomepart of · A chromosome is an encoded representation of a candidate solution in an evolutionary algorithm, composed of genes or other solution components.
- individualpart of · An individual is a single candidate solution represented as a member of an evolutionary algorithm's population and evaluated by its fitness.
- 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.
- codonhas part · A codon is a sequence of three nucleotides that specifies an amino acid.
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