What’s New?¶

Here is a (incomplete) log of the changes made to DEAP over time.

Release 0.9¶

Added forward compatibility to Python 3.2
Replaced varSimple() and varLambda() variation operators for the more specific varAnd() and varOr() operators.
Added a logging facility (EvolutionLogger) that produce easier to read console logging and a utility to transform that output into a Python dictionary.
Introduced the exact NSGA-II algorithm as described in Deb et al., 2002, A Fast Elitist Multiobjective Genetic Algorithm: NSGA-II.
NSGA-II selection algorithm revisited :
- Added a C++ version;
- Speed up of the Python version (up to 5x when the objectives are discrete).
Added some new benchmarks (multiobjective, binary and moving peaks).
Added translation, rotation, scaling and noise decorators to enhance benchmarks.

Modified structure so that DTM is a module of DEAP.
Restructured modules in a more permanent and coherent way.
- The toolbox is now in the module base.
- The operators have been moved to the tools module.
- Checkpoint, Statistics, History and Hall-of-Fame are now also in the tools module.
- Moved the GP specific operators to the gp module.
Renamed some operator for coherence.
Reintroduced a convenient, coherent and simple Statistics module.
Changed the Milestone module name for the more common Checkpoint name.
Eliminated the confusing content_init and size_init keywords in the toolbox.
Refactored the whole documentation in a more structured manner.
Added a benchmark module containing some of the most classic benchmark functions.
Added a lot of examples again :
- Differential evolution (x2);
- Evolution strategy : One fifth rule;
- k-nearest neighbours feature selection;
- One Max Multipopulation;
- Particle Swarm Optimization;
- Hillis’ coevolution of sorting networks;
- CMA-ES $1+\lambda$ .

Operator modify in-place the individuals (simplify a lot the algorithms).
Toolbox now contains two basic methods, map and clone that are useful in the algorithms.
The two methods can be replaced (as usual) to modify the behaviour of the algorithms.
Added new module History (compatible with NetworkX).
Genetic programming is now possible with Automatically Defined Functions (ADFs).
Algorithms now refers to literature algorithms.
Added new examples :
- Coevolution;
- Variable length genotype;
- Multiobjective;
- Inheriting from a Set;
- Using ADFs;
- Multiprocessing.
Basic operators can now be enhanced with decorators to do all sort of funny stuff.

Added a new module Milestone.
Enhanced Fitness efficiency when comparing fitnesses.
Replaced old base types with python built-in types.
Added an example of deriving from sets.
Added SPEA-II algorithm.
Fitnesses are no more extended when assigning value, the values are simply assigned.