You can find in this repository the source code of the algorithms implemented for the scientific papers listed:
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Francisco Chicano, Gabriela Ochoa, Lorenzo Canonne and Bilel Derbel, "Local Optima Markov Chains: A New Tool for Landscape-aware Analysis of Algorithm Dynamics", GECCO 2023,https://doi.org/10.1145/3583131.3590422. The exact version of the code used in the paper is in the commit tagged with GECCO2023 in the branch loma.
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Francisco Chicano, Gabriela Ochoa, Darrell Whitley, Renato Tinós, "Dynastic Potential Crossover Operator", Evolutionary Computation Journal 30 (3): 409-446 (https://doi.org/10.1162/evco_a_00305) Open Access. The code for this paper is in branch dpx-ecj, concretely, in tag ECJ2022-DPX.
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Gabriela Ochoa, Francisco Chicano and Marco Tomassini, "Global Landscape Structure and the Random MAX-SAT Phase Transition", PPSN 2020 (https://dx.doi.org/10.1007/978-3-030-58115-2_9)
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Gabriela Ochoa and Francisco Chicano, "Local Optima Network Analysis for MAX-SAT", Landscape-Aware Heuristics Search Workshop at GECCO 2019 (https://doi.org/10.1145/3319619.3326855)
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Francisco Chicano, Gabriela Ochoa, Darrell Whitley and Renato Tinós, "Quasi-Optimal Recombination Operator", EvoCOP 2019 (https://doi.org/10.1007/978-3-030-16711-0_9)
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Francisco Chicano, Gabriela Ochoa, Darrell Whitley and Renato Tinós, "Enhancing Partition Crossover with Articulation Points Analysis", GECCO 2018 (https://doi.org/10.1145/3205455.3205561)
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Francisco Chicano, Darrell Whitley, Gabriela Ochoa and Renato Tinós, "Optimizing One Million Variable NK Landscapes by Hybridizing Deterministic Recombination and Local Search", GECCO 2017 (https://doi.org/10.1145/3071178.3071285)
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Francisco Chicano, Darrell Whitley and Renato Tinós, "Efficient Hill Climber for Constrained Pseudo-Boolean Optimization Problems", GECCO 2016 (https://doi.org/10.1145/2908812.2908869)
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Francisco Chicano, Darrell Whitley and Renato Tinós, "Multi-Objective Pseudo-Boolean Optimization", EvoCOP 2016 (http://dx.doi.org/10.1007/978-3-319-30698-8_7)
In the following sections you will find instructions to run the algorithms in the papers. The name of the jar file generated by this commit is EfficientHillClimbers-0.18-LONSUPGRADE.jar
The detailed instructions to run the code in the paper can be found in the dpx-ecj branch.
In order to run the algorithms you need first to compile and package them into a JAR file (see instructions at the bottom of the page). Then, you can run them with the following commands:
java -jar <jarfile name> drils -problem maxsat -crossover none -improvingLo -lon -mf 0.1 -Pn=50 -Ppseed=<pseed> -Pm=<m> -Pmaxk=3 -Pmink=3 -time 60 -r 1 -aseed <aseed>
where <m> is the number of clauses (from 140 to 250 in steps of 10 in this paper) and <pseed> is the random seed to generate the instance (from 1 to 10 for each value of m in this paper). The previous command was repeated 20 times using a different algorithm random seed, established with the -aseed option. In this paper the algorithm random seed used for the 20 independent runs of the random instance determined by <m> and <pseed> goes from (<m>+<pseed>-141)*20 to (<m>+<pseed>-141)*20+19.
For computing the WalkSAT results we used WalkSat 5.6, available at https://gitlab.com/HenryKautz/Walksat/-/tree/master/Walksat_v56.
In order to run the algorithms you need first to compile and package them into a JAR file (see instructions at the bottom of the page). Then, you can run them with the following commands:
- For random MAX-SAT instances
java -jar <jarfile name> drils -problem maxsat -crossover none -Pn=40 -Ppseed=<m> -Pm=<m> -Pmaxk=3 -time 60 -r 1 -mf 0.05 -lon -improvingLo
where <m> is the number of clauses. The previous command can be repeated 100 times to generate the 100 independent runts. Use the -aseed option to establish the algorithm random seed.
- For maxcut instances
java -jar <jarfile name> drils -problem maxsat -crossover none -Pinstances=<instance> -time 60 -r 1 -mf 0.05 -lon -improvingLo
where <instance> is the file of the instance to be used in wcnf.gz format.
In order to run the algorithms you need first to compile and package them into a JAR file (see instructions at the bottom of the page). Then, you can run them with the following commands:
- For DRILS+DPX solving NKQ Landscapes
java -jar <jarfile name> drils+dpx-nk <options omitted>
The instances were randomly generated using the options. The seed for generating the instances was set using the -pseed option with values from 1 to 10.
- For DRILS+DPX solving MAX-SAT
java -jar <jarfile name> drils+dpx-maxsat <options omitted>
For the experiments of the paper some of the MAX-SAT weighted and unweighted incomplete benchmark was used. The ZIP files with the instances can be found at http://mse17.cs.helsinki.fi/benchmarks.html. The MAX-SAT weighted instances used in the experiments are listed in file maxsat-instances-weigthed-evocop2019.txt. The MAX-SAT unweighted instances used in the experiments are listed in file maxsat-instances-unweigthed-evocop2019.txt.
- For DRILS with PX or APX see the instructions below for the GECCO 2018 paper
To see the options, just run the algorithms without options.
In order to run the algorithms you need first to compile and package them into a JAR file (see instructions at the bottom of the page). Then, you can run them with the following commands:
- For DRILS+APX solving NKQ Landscapes
java -jar <jarfile name> drils+apx-nk <options omitted>
- For DRILS+APX solving MAX-SAT
java -jar <jarfile name> drils+apx-maxsat <options omitted>
- For DRILS (using PX) solving NKQ Landscapes
java -jar <jarfile name> drils-nk <options omitted>
- For DRILS (using PX) solving MAX-SAT
java -jar <jarfile name> drils-maxsat <options omitted>
To see the options, just run the algorithms without options.
Optimizing One Million Variable NK Landscapes by Hybridizing Deterministic Recombination and Local Search (GECCO 2017)
In order to run the algorithms you need first to compile and package them into a JAR file (see instructions at the bottom of the page). Then, you can run them with the following commands:
- For HiReLS
java -jar <jarfile name> px <options omitted>
- For DRILS
java -jar <jarfile name> rball+ils+px <options omitted>
To see the options, just run the algorithms without options.
In order to run the Multi-Objective Hamming Ball Hill Climber of EvoCOP 2016 select the "mo-hbhc" experiment using this string as the first argument after the JAR file name:
java -jar <jarfile name> mo-hbhc
In order to run the Hill Climber for Constrained Problems (explained in the GECCO 2016 paper), you should select the "moc-hbhc" experiment:
java -jar <jarfile name> moc-hbhc
If you run without any other argument, a list of all the options will appear with an explanation of their meaning.
Note: After submitting the camera-ready of the EvoCOP 2016 paper, a bug was found that could affect the results. In this branch the bug is fixed. The exact version used for the experiments in the EvoCOP 2016 paper is commit: https://github.com/jfrchicanog/EfficientHillClimbers/commit/213036ef593bb25617b48dcb58e81097a3437d71 .
The repository contains Java code shipped as a maven project. Maven 3 is needed as prerequisite. In order to build the executable JAR file open the command line and write:
mvn clean package
This will generate a JAR file in the target directory with all the dependencies included. To run the JAR file write:
java -jar <jarfile name>
The list of options are the different experiments available.