9. Covariance Matrix Adaptation Evolution Strategy¶

eap.cma.esCMA(toolbox, population, sigma, ngen[, halloffame, **kargs])¶

The CMA-ES algorithm as described in Hansen, N. (2006). The CMA Evolution Strategy: A Comparing Rewiew.

The provided population should be a list of one or more individuals. The other keyworded arguments are passed to the class CMAStrategy.

class eap.cma.CMAStrategy(population, sigma[, params])¶

Additional configuration may be passed throught the params argument as a dictionary,

Parameter	Default	Details
`lambda_`	`floor(4 + 3 * log(N))`	Number of children to produce at each generation, `N` is the individual’s size.
`mu`	`floor(lambda_ / 2)`	The number of parents to keep from the lambda children.
`weights`	`"superlinear"`	Decrease speed, can be `"superlinear"`, `"linear"` or `"equal"`.
`cs`	`(mueff + 2) / (N + mueff + 3)`	Cumulation constant for step-size.
`damps`	`1 + 2 * max(0, sqrt(( mueff - 1) / (N + 1)) - 1) + cs`	Damping for step-size.
`ccum`	`4 / (N + 4)`	Cumulation constant for covariance matrix.
`ccov1`	`2 / ((N + 1.3)^2 + mueff)`	Learning rate for rank-one update.
`ccovmu`	`2 * (mueff - 2 + 1 / mueff) / ((N + 2)^2 + mueff)`	Learning rate for rank-mu update.

computeParams(params)¶: Those parameters depends on lambda and need to computed again if it changes during evolution.

generate()¶: Generate lambda offsprings from the current strategy using the centroid individual as parent.