• ./gradlew build builds into .class files.
• ./gradlew jar builds jar file for the library.

This repository has examples written in Jupyter Notebooks. Before running, jar must be built first and requirements need to be installed (primarily `kotlin-jupyter-kernel``)

./gradlew jar
pip install -r requirements.txt
jupyter-notebook

This library is to serve as a framework for performing Stochastic Local Search (SLS) based on Generalised Local Search Machines (GLSM). It was shown that best performing SLS (on a variety of classes of problems) are combinations of multiple pure SLS algorithms. The idea of GLSMs is a unified way to build a represent SLS algorithms that may (or may not be) combinations of pure strategies themselves, using Finite State Automata.

A GLSM consists of:

• State machine with Search Strategies as nodes and Transition Predicates as transitions between them
• Memory that may hold current and best solutions, steps performed and other task or search strategy dependent information
• Search Space definition, and possible operations on it
• Problem definition, or in other words cost function

Note: In SLS 2005, the last two components are treated as inputs to GLSM instead, but for the sake of convinience I will be sticking to the definition above. Subject to change later.

This is WIP, the signatures most likely will be changed in the future, until I settle down on what works best. More algorithms, search space supports, and model problems are yet to be added.

[SLS 2005] H. H. Hoos and Stützle Thomas, Stochastic local search: foundations and applications. Amsterdam: Morgan Kaufmann Publishers, 2005. -- Main reference for SLS algorithms and GLSM framework. If not explicitly stated otherwise, you can assume that it's from this book.

[SURVEY 2012] Parejo, J.A., Ruiz-Cortés, A., Lozano, S. et al. Metaheuristic optimization frameworks: a survey and benchmarking. Soft Comput 16, 527–561 (2012). https://doi.org/10.1007/s00500-011-0754-8 -- a good review and a benchmark of existing libraries for metaheuristics/SLS algorithms. More comparisons with this approach against the pre-existing once are to come.