πbaumhaus.digital/Design & Computation/Perspectives in Engineering/IOPS/Heuristics/Nature-inspired heuristics/Evolutionary optimization (is_parent) weight 1β
Attributes
creator
::
daniel-hromada
public
::
1
epoch
::
1701162903
baumhaus.digital/Design & Computation/Perspectives in Engineering/IOPS/Nature-inspired heuristics/Evolutionary optimization/Genetic Algorithm
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.
3 Axones
β
this knot is_parent πbaumhaus.digital/Design & Computation/Perspectives in Engineering/IOPS/Nature-inspired heuristics/Evolutionary optimization/Genetic Algorithm/Replication
(ID: 1201 :: weight 1)
β
this knot is_parent πbaumhaus.digital/Design & Computation/Perspectives in Engineering/IOPS/Nature-inspired heuristics/Evolutionary optimization/Genetic Algorithm/Variation
(ID: 1202 :: weight 1)
β
this knot is_parent πbaumhaus.digital/Design & Computation/Perspectives in Engineering/IOPS/Nature-inspired heuristics/Evolutionary optimization/Genetic Algorithm/Selection
(ID: 1203 :: weight 1)