Chips-n-Salsa

[Mentioned in Awesome Machine Learning]

Overview

Chips-n-Salsa is a Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms. The library includes implementations of several stochastic local search algorithms, including simulated annealing, hill climbers, as well as constructive search algorithms such as stochastic sampling. Chips-n-Salsa now also includes genetic algorithms as well as evolutionary algorithms more generally. The library very extensively supports simulated annealing. It includes several classes for representing solutions to a variety of optimization problems. For example, the library includes a BitVector class that implements vectors of bits, as well as classes for representing solutions to problems where we are searching for an optimal vector of integers or reals. For each of the built-in representations, the library provides the most common mutation operators for generating random neighbors of candidate solutions, as well as common crossover operators for use with evolutionary algorithms. Additionally, the library provides extensive support for permutation optimization problems, including implementations of many different mutation operators for permutations, and utilizing the efficiently implemented Permutation class of the JavaPermutationTools (JPT) library.

Chips-n-Salsa is customizable, making extensive use of Java's generic types, enabling using the library to optimize other types of representations beyond what is provided in the library. It is hybridizable, providing support for integrating multiple forms of local search (e.g., using a hill climber on a solution generated by simulated annealing), creating hybrid mutation operators (e.g., local search using multiple mutation operators), as well as support for running more than one type of search for the same problem concurrently using multiple threads as a form of algorithm portfolio. Chips-n-Salsa is iterative, with support for multistart metaheuristics, including implementations of several restart schedules for varying the run lengths across the restarts. It also supports parallel execution of multiple instances of the same, or different, stochastic local search algorithms for an instance of a problem to accelerate the search process. The library supports self-adaptive search in a variety of ways, such as including implementations of adaptive annealing schedules for simulated annealing, such as the Modified Lam schedule, implementations of the simpler annealing schedules but which self-tune the initial temperature and other parameters, and restart schedules that adapt to run length.

The Chips-n-Salsa source code repository is hosted on GitHub; and is licensed under the GNU General Public License Version 3 (GPLv3). The API documentation is found on this site. The library is regularly published to Maven Central, from which it is easily imported into software projects using Maven and other commonly used build tools.

Additionally, there is a GitHub repository of example programs that show basic usage of the Chips-n-Salsa library.

The Chips-n-Salsa library originated as part of the research of Vincent A. Cicirello.

How to Cite

If you use the Chips-n-Salsa library in your research, please cite the following article which describes the library:

Importing from Maven Central

To import Chips-n-Salsa from the Maven Central repository (if your build tool is Maven), add the following to the dependencies section of your “pom.xml” (for other build tools, see your build tool's documentation for the equivalent):

<dependency>
  <groupId>org.cicirello</groupId>
  <artifactId>chips-n-salsa</artifactId>
  <version>x.y.z</version>
</dependency>

Just replace x.y.z with your desired version of the library.

Java Modules

The library provides a Java module, org.cicirello.chips_n_salsa. If your project uses Java modules, then add the following to your module-info.java:


module your.module.name.here {
    requires org.cicirello.chips_n_salsa;
}

Chips-n-Salsa transitively requires the org.cicirello.jpt module from its dependency on the JavaPermutationTools (JPT) library because the functionality for optimizing permutation problems accepts Permutation objects, defined in JPT, as parameters to methods and returns such objects from other methods. So if your project utilizes any functionality from JPT, you shouldn't need to explicitly require the org.cicirello.jpt module. JPT in turn transitively requires the modules provided by the ρμ and org.cicirello.core libraries. Your dependency manager (see previous section) will handle downloading these for you.

Semantic Versioning

Chips-n-Salsa uses Semantic Versioning with version numbers of the form: MAJOR.MINOR.PATCH, where differences in MAJOR correspond to incompatible API changes, differences in MINOR correspond to introduction of backwards compatible new functionality, and PATCH corresponds to backwards compatible bug fixes.

Development Process

To maintain a high quality library, our development process utilizes the following:

Related Publications

The following is a list of publications that either influenced the design of the Chips-n-Salsa library, or which directly used code from the library. In many cases, the papers in the list used earlier implementations of library contents, but which has since been reimplemented. For example, much of the library's code was originally written specifically for permutation optimization problems (i.e., optimization problems, such as the traveling salesperson, for which we are searching the space of permutations). However, much of this code has been heavily refactored (in some cases rewritten from scratch) to use Java generic types enabling application to many other types of optimization problem, including real-valued function optimization. Additionally, the code in the Chips-n-Salsa library has been optimized for execution speed, such as utilizing more efficient random number generators than was used in some of these prior publications.