Roadmap

The LEAP development roadmap is as follows:

  1. pre-Minimally Viable Product – released 1/14/2020 as 0.1-pre
    • basic support for binary representations

      • bit flip mutation

      • point-wise crossover

      • uniform crossover

    • basic support for real-valued representations

      • mutate gaussian

    • selection operators

      • truncation selection

      • tournament_selection selection

      • random selection

      • deterministic cyclic selection

      • insertion selection

    • continuous integration via Travis

    • common test functions
      • binary

        • MAXONES

      • real-valued, optionally translated, rotated, and scaled

        • Ackley

        • Cosine

        • Griewank

        • Langermann

        • Lunacek

        • Noisy Quartic

        • Rastrigin

        • Rosenbock

        • Schwefel

        • Shekel

        • Spheroid

        • Step

        • Weierstrass

    • test harnesses

      • pytest supported

    • simple usage examples
      • canonical EAs

        • genetic algorithms (GA)

        • evolutionary programming (EP)

        • evolutionary strategies (ES)

      • simple island model

      • basic run-time visualizations

      • use with Jupyter notebooks

    • documentation outline/stubs for ReadTheDocs

  2. Minimally Viable Product, second part – released 6/14/2020 as 0.2.0
    • distributed / parallel fitness evaluations

      • distribute local cores vs. distributed cluster nodes

      • synchronous vs. asynchronous evaluations

    • variable-length genomes

    • Gray encoding

  3. Future features, in no particular order of priority

    • parsimony pressure

    • multi-objective optimization

    • minimally complete documentation

      • fleshed out ReadTheDocs documentation

      • technical report

    • checkpoint / restart support

    • hall of fame

    • Rule systems

      • Mich Approach

      • Pitt Approach

    • Genetic Programming (GP)

    • Estimation of Distribution Algorithms (EDA)

      • Covariance Matrix Adaptation Evolution Strategy (CMA-ES)

      • Population-based Incremental Learning (PBIL)

      • Bayesian Optimization Algorithm (BOA)