Implement Evolution Strategy Emitter

[itsdawei]

Description

This PR is a split from #207

This PR introduces major API changes for the Emitter class. Emitter class now requires a Ranker object and a Selector object.

The Ranker object will define a rank function which returns the index of the solutions in the descending order that they should be in. It will also define a reset function which resets the internal states of the object.

We also introduce FilterSelector and MuSelector objects which defines a select() function that decides how many parents to select from the potential parents.

Introduced EvolutionStrategyEmitter(x0, sigma0, ranker, restart_rule, bounds, batch_size, seed), which can serve as
RandomDirectionEmitter, OptimizingEmitter, and ImprovementEmitter depending on the ranker object.

Bugs Discovered

• We passed in the same RandomDirectionRanker to the emitters in sphere example, causing the emitter to fail because the ranker is supposed to maintain different random directions.
• RandomDirectionRanker was sorting the solutions using the wrong keys.

TODO

• Implement EvolutionStrategyEmitter
• Pytest
• Test examples/sphere
• Sphinx docs
• Fix bugs that we discovered
• Describe bugs discovered (see above)

Status

• I have read the guidelines in CONTRIBUTING.md.
• I have formatted my code using yapf
• I have tested my code by running pytest
• I have linted my code with pylint
• I have added a one-line description of my change to the changelog in HISTORY.md.
• This PR is ready to go

[itsdawei]

For reference, we are gonna leave a table that shows the performance (archive coverage, QD score, and time) of the previous implementation using different emitters and the new implementation that adapts a emitter-ranker structure (using only the evolution_strategy_emitter).

Algorithm	New Implementation	Old Implementation
cma_me_imp	91.050%, 17390451 QD, 141s	91.072%, 17475874 QD, 102s
cma_me_imp_mu	94.098%, 17793410 QD, 140s	92.986%, 17683259 QD, 104s
cma_me_rd	97.362%, 15219340 QD, 120s	92.061%, 12029980 QD, 110s
cma_me_rd_mu	97.172%, 12869971 QD, 114s	95.455%, 11579076 QD, 107s
cma_me_opt	12.224%, 2542591 QD, 112s	12.495%, 2619242 QD, 96s
cma_me_mixed	95.640%, 17942995 QD, 109s	95.576%, 17841624 QD, 106s

We note that our new implementation has similar coverage and QD score compared to that of the old implementation. Hence, we justify that our new implementation is algorithmically equivalent to the old implementation.