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Genetic Algorithm

🌐 public · created AE531128 (28.11.2023) · by DDH · open in the standard editor view · 📽 open as presentation

A Genetic Algorithm is a method in evolutionary optimization that solves problems by mimicking natural evolution. Imagine a survival contest where each participant (solution) has traits (parameters). These solutions breed and mutate, creating new generations. The fittest solutions, judged by a fitness function, survive to breed again. Over time, this process 'evolves' increasingly effective solutions. It's like nature's trial-and-error but used for complex problems like route planning, where finding the best or a good-enough solution is essential.

Ancestors (1 superordinated path)

root/ baumhaus.digital/ Design & Computation/ Perspectives in Engineering/ IOPS/ Heuristics/ Nature-inspired heuristics/ Evolutionary optimization/ Genetic Algorithm

Descendants (at least 10 branches originate here)

  • Genetic Algorithm
    A Genetic Algorithm is a method in evolutionary optimization that solves problems by mimicking natural evolution. Imagine a survival contest where each particip
    • is_parent Replication
      In evolutionary optimization, replication is like making copies of the best solutions. Imagine a survival contest where top performers are cloned. These copies
      • is_parent Individual|Genotype|Chromosome ·
        In evolutionary optimization, an "individual," also termed a "genotype" or "chromosome," is a candidate solution to a problem. Think of it like a recipe where e
      • is_parent Population ·
        Population is a set of individuals.
    • is_parent Variation
      In evolutionary optimization, variation is the process of introducing diversity into the population of solutions. Like genetic mutations and breeding in nature,
      • is_parent Mutation ·
        for numeric genes: additive mutation, multiplicative mutation, complex (imaginary) mutation for symbolic ones: removal, addition or replacement of a symbol; met
      • is_parent Crossover ·
        Crossover
    • is_parent Selection
      In evolutionary optimization, selection is like a survival test for candidate solutions, deciding which ones get to 'reproduce.' Selection operators are the rul
      • is_parent Selection operators ·
        elitism: select N most fit individuals and copy them to next generation roulette-wheel: probability of survival into next generation is proportional to indvidid
      • is_parent Fitness function ·
        In evolutionary optimization, a "fitness function" is like a scoring system that rates how good each candidate solution (or 'individual') is at solving the prob
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