=========== What's New? =========== Here is a (incomplete) log of the changes made to DEAP over time. Release 0.8 =========== - Added forward compatibility to Python 3.2 - Replaced :func:`~deap.algorithms.varSimple` and :func:`~deap.algorithms.varLambda` variation operators for the more specific :func:`~deap.algorithms.varAnd` and :func:`~deap.algorithms.varOr` operators. - Added a logging facility (:class:`~deap.tools.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. Release 0.7 =========== - 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 :math:`1+\lambda`. Release 0.6 =========== - 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. Release 0.5 =========== - 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.