dacbench.envs.theory

Module Contents

Classes

BinaryProblem	An abstract class for an individual in binary representation
LeadingOne	An individual for LeadingOne problem
RLSEnv	Environment for RLS with step size
RLSEnvDiscrete	RLS environment where the choices of r is discretised

class dacbench.envs.theory.BinaryProblem(n, rng=np.random.default_rng())

An abstract class for an individual in binary representation

initialise_with_fixed_number_of_bits(self, k, rng=np.random.default_rng())

is_optimal(self)

get_optimal(self)

eval(self)

abstract get_fitness_after_flipping(self, locs)

Calculate the change in fitness after flipping the bits at positions locs

Parameters

locs (1d-array) – positions where bits are flipped

objective after flipping

abstract get_fitness_after_crossover(self, xprime, locs_x, locs_xprime)

Calculate fitness of the child aftering being crossovered with xprime

Parameters

• xprime (1d boolean array) – the individual to crossover with

• locs_x (1d boolean/integer array) – positions where we keep current bits of self

• locs_xprime (: 1d boolean/integer array) – positions where we change to xprime’s bits

flip(self, locs)

flip the bits at position indicated by locs

Parameters

locs (1d-array) – positions where bits are flipped

Returns: the new individual after the flip

combine(self, xprime, locs_xprime)

combine (crossover) self and xprime by taking xprime’s bits at locs_xprime and self’s bits at other positions

Parameters

• xprime (1d boolean array) – the individual to crossover with

• locs_x (1d boolean/integer array) – positions where we keep current bits of self

• locs_xprime (: 1d boolean/integer array) – positions where we change to xprime’s bits

Returns: the new individual after the crossover

mutate(self, p, n_childs, rng=np.random.default_rng())

Draw l ~ binomial(n, p), l>0 Generate n_childs children by flipping exactly l bits Return: the best child (maximum fitness), its fitness and number of evaluations used

mutate_rls(self, l, rng=np.random.default_rng())

generate a child by flipping exactly l bits Return: child, its fitness

crossover(self, xprime, p, n_childs, include_xprime=True, count_different_inds_only=True, rng=np.random.default_rng())

Crossover operator:

for each bit, taking value from x with probability p and from self with probability 1-p

Arguments:

x: the individual to crossover with p (float): in [0,1]

class dacbench.envs.theory.LeadingOne(n, rng=np.random.default_rng(), initObj=None)

Bases: dacbench.envs.theory.BinaryProblem

An individual for LeadingOne problem The aim is to maximise the number of leading (and consecutive) 1 bits in the string

eval(self)

is_optimal(self)

get_optimal(self)

get_fitness_after_flipping(self, locs)

Calculate the change in fitness after flipping the bits at positions locs

Parameters

locs (1d-array) – positions where bits are flipped

objective after flipping

get_fitness_after_crossover(self, xprime, locs_x, locs_xprime)

Calculate fitness of the child aftering being crossovered with xprime

Parameters

• xprime (1d boolean array) – the individual to crossover with

• locs_x (1d boolean/integer array) – positions where we keep current bits of self

• locs_xprime (: 1d boolean/integer array) – positions where we change to xprime’s bits

dacbench.envs.theory.MAX_INT = 100000000.0

dacbench.envs.theory.HISTORY_LENGTH = 5

class dacbench.envs.theory.RLSEnv(config, test_env=False)

Bases: dacbench.AbstractEnv

Environment for RLS with step size Current assumption: we only consider (1+1)-RLS, so there’s only one parameter to tune (r)

get_obs_domain_from_name(var_name)

Get default lower and upperbound of a observation variable based on its name. The observation space will then be created Return:

Two int values, e.g., 1, np.inf

reset(self)

Resets env

Returns

Environment state

Return type

numpy.array

get_state(self)

step(self, action)

Execute environment step

Parameters

action (Box) – action to execute

Returns

• state, reward, done, info

• np.array, float, bool, dict

close(self) → bool

Close Env

No additional cleanup necessary

Returns

Closing confirmation

Return type

bool

class dacbench.envs.theory.RLSEnvDiscrete(config, test_env=False)

Bases: dacbench.envs.theory.RLSEnv

RLS environment where the choices of r is discretised

step(self, action)

Execute environment step

Parameters

action (Box) – action to execute

Returns

• state, reward, done, info

• np.array, float, bool, dict