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Survival method

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

A survival method determines which individuals remain in the population for the next generation.

A survival method determines which individuals remain in an evolutionary-computation population for the next generation. It governs which candidate solutions continue after reproduction. section 7.2.3

Use survival methods as one part of the evolutionary-computation cycle:

  • Evaluate individuals with a fitness value, where higher fitness indicates a better solution. section 7.2.3
  • Select promising parents, create offspring through crossover and mutation, then decide which individuals advance. section 7.2.3
  • Recognize that survival selection varies across evolutionary-computation paradigms alongside representation and reproduction choices. section 7.2.3
Figure 7.5 EC paradigms

Compare how evolutionary-computation paradigms differ in survival selection, individual representation, and genetic operators. section 7.2.3

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

How deep it goes
Defines generational survival selection but does not specify particular survival algorithms or parameter heuristics.
Examples
Uses Figure 7.5 to compare evolutionary-computation paradigms and their differing survival-selection approaches.
What it assumes
Assumes basic familiarity with populations, candidate solutions, fitness, parent selection, crossover, and mutation.
What you can do after
Evaluate how candidate solutions continue into the next generation within an evolutionary-computation algorithm.
How it connects
Connects survival selection with fitness evaluation, parent selection, crossover, mutation, and evolutionary-computation paradigms.
Cover of Optimization Algorithms: AI techniques for design, planning, and control problems
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