# customhys
<div>
<img align="left" alt="Module Dependency Diagram" src="https://raw.githubusercontent.com/jcrvz/customhys/master/docfiles/chm_logo.png" title="Customhys logo" width="25%"/>
</div>
<div align="justify">
<b>Customising optimisation metaheuristics via hyper-heuristic search</b> (CUSTOMHyS). This framework provides tools for solving, but not limited to, continuous optimisation problems using a hyper-heuristic approach for customising metaheuristics. Such an approach is powered by a strategy based on Simulated Annealing. Also, several search operators serve as building blocks for tailoring metaheuristics. They were extracted from ten well-known metaheuristics in the literature.
</div>
Detailed information about this framework can be found in [[1, 2]](#references). Plus, the code for each module is well-documented.
### 🛠 Requirements:
| Package | Version (>=) |
|----------------------------------------------------|--------------|
| [Python](https://github.com/conda-forge/miniforge) | 3.8 |
| [NumPy](https://numpy.org) | 1.22.0 |
| [SciPy](https://scipy.org) | 1.5.0 |
| [matplotlib](https://matplotlib.org) | 3.2.2 |
| [tqdm](https://tqdm.github.io) | 4.47.0 |
| [pandas](https://pandas.pydata.org) | 1.5.3 |
| [scikit-learn](https://scikit-learn.org/stable/) | 1.2.2 |
| [TensorFlow](https://www.tensorflow.org)* | 2.8.0 |
*For Mac M1/M2, one may need to install TensorFlow via `conda` such as:
```shell
conda install -c apple tensorflow-deps
```
Further information can be found at [Install TensorFlow on Mac M1/M2 with GPU support](https://medium.com/mlearning-ai/install-tensorflow-on-mac-m1-m2-with-gpu-support-c404c6cfb580) by D. Ganzaroli.
## 🧰 Modules
The modules that comprise this framework depend on some basic Python packages, as well as they liaise each other. The module dependency diagram is presented as follows:
**NOTE:** Each module is briefly described below. If you require further information, please check the corresponding source code.
### 🤯 Problems (benchmark functions)
This module includes several benchmark functions as classes to be solved by using optimisation techniques. The class structure is based on Keita Tomochika's repository [optimization-evaluation](https://github.com/keit0222/optimization-evaluation).
Source: [``benchmark_func.py``](customhys/benchmark_func.py)
### 👯♂️ Population
This module contains the class Population. A Population object corresponds to a set of agents or individuals within a problem domain. These agents themselves do not explore the function landscape, but they know when to update the position according to a selection procedure.
Source: [``population.py``](customhys/population.py)
### 🦾 Search Operators (low-level heuristics)
This module has a collection of search operators (simple heuristics) extracted from several well-known metaheuristics in the literature. Such operators work over a population, i.e., modify the individuals' positions.
Source: [``operators.py``](customhys/operators.py)
### 🤖 Metaheuristic (mid-level heuristic)
This module contains the Metaheuristic class. A metaheuristic object implements a set of search operators to guide a population in a search procedure within an optimisation problem.
Source: [``metaheuristic.py``](customhys/metaheuristic.py)
### 👽 Hyper-heuristic (high-level heuristic)
This module contains the Hyperheuristic class. Similar to the Metaheuristic class, but in this case, a collection of search operators is required. A hyper-heuristic object searches within the heuristic space to find the sequence that builds the best metaheuristic for a specific problem.
Source: [``hyperheuristic.py``](customhys/hyperheuristic.py)
### 🏭 Experiment
This module contains the Experiment class. An experiment object can run several hyper-heuristic procedures for a list of optimisation problems.
Source: [``experiment.py``](customhys/experiment.py)
### 🗜️ Tools
This module contains several functions and methods utilised by many modules in this package.
Source: [``tools.py``](customhys/tools.py)
### 🧠 Machine Learning
This module contains the implementation of Machine Learning models which can power a hyper-heuristic model from this framework. In particular, it is implemented a wrapper for a Neural Network model from Tensorflow. Also, contains auxiliar data structures which process sample of sequences to generate training data for Machine Learning models.
Source: [``machine_learning.py``](customhys/machine_learning.py)
### 💾 Data Structure
The experiments are saved in JSON files. The data structure of a saved file follows a particular scheme described below.
<details>
<summary> Expand structure </summary>
<p>
```
data_frame = {dict: N}
|-- 'problem' = {list: N}
| |-- 0 = {str}
: :
|-- 'dimensions' = {list: N}
| |-- 0 = {int}
: :
|-- 'results' = {list: N}
| |-- 0 = {dict: 6}
| | |-- 'iteration' = {list: M}
| | | |-- 0 = {int}
: : : :
| | |-- 'time' = {list: M}
| | | |-- 0 = {float}
: : : :
| | |-- 'performance' = {list: M}
| | | |-- 0 = {float}
: : : :
| | |-- 'encoded_solution' = {list: M}
| | | |-- 0 = {int}
: : : :
| | |-- 'solution' = {list: M}
| | | |-- 0 = {list: C}
| | | | |-- 0 = {list: 3}
| | | | | |-- search_operator_structure
: : : : : :
| | |-- 'details' = {list: M}
| | | |-- 0 = {dict: 4}
| | | | |-- 'fitness' = {list: R}
| | | | | |-- 0 = {float}
: : : : : :
| | | | |-- 'positions' = {list: R}
| | | | | |-- 0 = {list: D}
| | | | | | |-- 0 = {float}
: : : : : : :
| | | | |-- 'historical' = {list: R}
| | | | | |-- 0 = {dict: 5}
| | | | | | |-- 'fitness' = {list: I}
| | | | | | | |-- 0 = {float}
: : : : : : : :
| | | | | | |-- 'positions' = {list: I}
| | | | | | | |-- 0 = {list: D}
| | | | | | | | |-- 0 = {float}
: : : : : : : : :
| | | | | | |-- 'centroid' = {list: I}
| | | | | | | |-- 0 = {list: D}
| | | | | | | | |-- 0 = {float}
: : : : : : : : :
| | | | | | |-- 'radius' = {list: I}
| | | | | | | |-- 0 = {float}
: : : : : : : :
| | | | | | |-- 'stagnation' = {list: I}
| | | | | | | |-- 0 = {int}
: : : : : : : :
| | | | |-- 'statistics' = {dict: 10}
| | | | | |-- 'nob' = {int}
| | | | | |-- 'Min' = {float}
| | | | | |-- 'Max' = {float}
| | | | | |-- 'Avg' = {float}
| | | | | |-- 'Std' = {float}
| | | | | |-- 'Skw' = {float}
| | | | | |-- 'Kur' = {float}
| | | | | |-- 'IQR' = {float}
| | | | | |-- 'Med' = {float}
| | | | | |-- 'MAD' = {float}
: : : : : :
```
where:
- ```N``` is the number of files within data_files folder
- ```M``` is the number of hyper-heuristic iterations (metaheuristic candidates)
- ```C``` is the number of search operators in the metaheuristic (cardinality)
- ```P``` is the number of control parameters for each search operator
- ```R``` is the number of repetitions performed for each metaheuristic candidate
- ```D``` is the dimensionality of the problem tackled by the metaheuristic candidate
- ```I``` is the number of iterations performed by the metaheuristic candidate
- ```search_operator_structure``` corresponds to ```[operator_name = {str}, control_parameters = {dict: P}, selector = {str}]```
</p>
</details>
## 🏗️ Work-in-Progress
The following modules are available, but they may do not work. They are currently under developing.
### 🌡️ Characterisation
This module intends to provide metrics for characterising the benchmark functions.
Source: [``characterisation.py``](customhys/characterisation.py)
### 📊 Visualisation
This module intends to provide several tools for plotting results from the experiments.
Source: [``visualisation.py``](customhys/visualisation.py)
## Sponsors
<a href="https://tec.mx/en" target="_blank"><img src="https://github.com/jcrvz/customhys/raw/master/docfiles/logoTEC_full.png" width="250"></a>
<a href="http://www.cas.cn/" target="_blank"><img src="https://github.com/jcrvz/customhys/raw/master/docfiles/cas_logo.png" width="250"></a>
<a href="https://www.gob.mx/conacyt" target="_blank"><img src="https://github.com/jcrvz/customhys/raw/master/docfiles/conacyt-logo.png" width="250"></a>
## References
1. [J. M. Cruz-Duarte, I. Amaya, J. C. Ortiz-Bayliss, H. Terashima-Marín, and Y. Shi, CUSTOMHyS: Customising Optimisation Metaheuristics via Hyper-heuristic Search, SoftwareX, vol. 12, p. 100628, 2020.](https://www.sciencedirect.com/science/article/pii/S2352711020303411)
1. [J. M. Cruz-Duarte, I. Amaya, J. C. Ortiz-Bayliss, S. E. Conant-Pablos, H. Terashima-Marín, H., and Y. Shi. _Hyper-Heuristics to Customise Metaheuristics for Continuous Optimisation_, *Swarm and Evolutionary Computation*, 100935.](https://doi.org/10.1016/j.swevo.2021.100935)
1. [J. M. Cruz-Duarte, I. Amaya, J. C. Ortiz-Bayliss, S. E. Connat-Pablos, and H. Terashima-Marín, A Primary Study on Hyper-Heuristics to Customise Metaheuristics for Continuous Optimisation. CEC'2020.](docfiles/SearchOperators_CEC.pdf)
1. [J. M. Cruz-Duarte, J. C. Ortiz-Bayliss, I. Amaya, Y. Shi, H. Terashima-Marín, and N. Pillay, Towards a Generalised Metaheuristic Model for Continuous Optimisation Problems, Mathematics, vol. 8, no. 11, p. 2046, Nov. 2020.](https://www.mdpi.com/2227-7390/8/11/2046)
1. [J. M. Cruz-Duarte, J. C. Ortiz-Bayliss, I. Amaya, and N. Pillay, _Global Optimisation through Hyper-Heuristics: Unfolding Population-Based Metaheuristics_, *Appl. Sci.*, vol. 11, no. 12, p. 5620, 2021.](http://dx.doi.org/10.3390/app11125620)
1. [J. M. Cruz-Duarte, I. Amaya, J. C. Ortiz-Bayliss, N. Pillay. Automated Design of Unfolded Metaheuristics and the Effect of Population Size. 2021 IEEE Congress on Evolutionary Computation (CEC), 1155–1162, 2021.](https://doi.org/10.1109/CEC45853.2021.9504879)
1. [J. M. Tapia-Avitia, J. M. Cruz-Duarte, I. Amaya, J. C. Ortiz-Bayliss, H. Terashima-Marin, and N. Pillay. _A Primary Study on Hyper-Heuristics Powered by Artificial Neural Networks for Customising Population-based Metaheuristics in Continuous Optimisation Problems_, 2022 IEEE Congress on Evolutionary Computation (CEC), 2022.](https://doi.org/10.1109/CEC55065.2022.9870275)
1. [J. M. Cruz-Duarte, I. Amaya, J. C. Ortiz-Bayliss, N. Pillay. _A Transfer Learning Hyper-heuristic Approach for Automatic Tailoring of Unfolded Population-based Metaheuristics_, 2022 IEEE Congress on Evolutionary Computation (CEC), 2022.](https://doi.org/10.1109/CEC55065.2022.9870426)
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"keywords": "metaheuristics, hyper-heuristic, optimization, automatic design, global optimization, evolutionary computation, bio-inspired, algorithm design",
"author": "Jorge Mario Cruz-Duarte",
"author_email": "jorge.cruz@tec.mx",
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"description": "# customhys\n\n<div>\n <img align=\"left\" alt=\"Module Dependency Diagram\" src=\"https://raw.githubusercontent.com/jcrvz/customhys/master/docfiles/chm_logo.png\" title=\"Customhys logo\" width=\"25%\"/>\n</div>\n<div align=\"justify\"> \n <b>Customising optimisation metaheuristics via hyper-heuristic search</b> (CUSTOMHyS). This framework provides tools for solving, but not limited to, continuous optimisation problems using a hyper-heuristic approach for customising metaheuristics. Such an approach is powered by a strategy based on Simulated Annealing. Also, several search operators serve as building blocks for tailoring metaheuristics. They were extracted from ten well-known metaheuristics in the literature.\n</div>\n\nDetailed information about this framework can be found in [[1, 2]](#references). Plus, the code for each module is well-documented.\n\n\n### \ud83d\udee0 Requirements:\n\n| Package | Version (>=) |\n|----------------------------------------------------|--------------|\n| [Python](https://github.com/conda-forge/miniforge) | 3.8 |\n| [NumPy](https://numpy.org) | 1.22.0 |\n| [SciPy](https://scipy.org) | 1.5.0 |\n| [matplotlib](https://matplotlib.org) | 3.2.2 |\n| [tqdm](https://tqdm.github.io) | 4.47.0 |\n| [pandas](https://pandas.pydata.org) | 1.5.3 |\n| [scikit-learn](https://scikit-learn.org/stable/) | 1.2.2 |\n| [TensorFlow](https://www.tensorflow.org)* | 2.8.0 |\n\n*For Mac M1/M2, one may need to install TensorFlow via `conda` such as:\n```shell\nconda install -c apple tensorflow-deps\n```\nFurther information can be found at [Install TensorFlow on Mac M1/M2 with GPU support](https://medium.com/mlearning-ai/install-tensorflow-on-mac-m1-m2-with-gpu-support-c404c6cfb580) by D. Ganzaroli.\n\n## \ud83e\uddf0 Modules\n\nThe modules that comprise this framework depend on some basic Python packages, as well as they liaise each other. The module dependency diagram is presented as follows:\n\n\n\n**NOTE:** Each module is briefly described below. If you require further information, please check the corresponding source code.\n\n### \ud83e\udd2f Problems (benchmark functions)\n\nThis module includes several benchmark functions as classes to be solved by using optimisation techniques. The class structure is based on Keita Tomochika's repository [optimization-evaluation](https://github.com/keit0222/optimization-evaluation).\n\nSource: [``benchmark_func.py``](customhys/benchmark_func.py)\n\n### \ud83d\udc6f\u200d\u2642\ufe0f Population\n\nThis module contains the class Population. A Population object corresponds to a set of agents or individuals within a problem domain. These agents themselves do not explore the function landscape, but they know when to update the position according to a selection procedure.\n\nSource: [``population.py``](customhys/population.py)\n\n### \ud83e\uddbe Search Operators (low-level heuristics)\n\nThis module has a collection of search operators (simple heuristics) extracted from several well-known metaheuristics in the literature. Such operators work over a population, i.e., modify the individuals' positions. \n\nSource: [``operators.py``](customhys/operators.py)\n\n### \ud83e\udd16 Metaheuristic (mid-level heuristic)\n\nThis module contains the Metaheuristic class. A metaheuristic object implements a set of search operators to guide a population in a search procedure within an optimisation problem.\n\nSource: [``metaheuristic.py``](customhys/metaheuristic.py)\n\n### \ud83d\udc7d Hyper-heuristic (high-level heuristic)\n\nThis module contains the Hyperheuristic class. Similar to the Metaheuristic class, but in this case, a collection of search operators is required. A hyper-heuristic object searches within the heuristic space to find the sequence that builds the best metaheuristic for a specific problem.\n\nSource: [``hyperheuristic.py``](customhys/hyperheuristic.py)\n\n### \ud83c\udfed Experiment\n\nThis module contains the Experiment class. An experiment object can run several hyper-heuristic procedures for a list of optimisation problems.\n\nSource: [``experiment.py``](customhys/experiment.py)\n\n### \ud83d\udddc\ufe0f Tools\n\nThis module contains several functions and methods utilised by many modules in this package.\n\nSource: [``tools.py``](customhys/tools.py)\n\n### \ud83e\udde0 Machine Learning\n\nThis module contains the implementation of Machine Learning models which can power a hyper-heuristic model from this framework. In particular, it is implemented a wrapper for a Neural Network model from Tensorflow. Also, contains auxiliar data structures which process sample of sequences to generate training data for Machine Learning models.\n\nSource: [``machine_learning.py``](customhys/machine_learning.py)\n\n### \ud83d\udcbe Data Structure\n\nThe experiments are saved in JSON files. The data structure of a saved file follows a particular scheme described below.\n\n<details>\n<summary> Expand structure </summary>\n<p>\n\n```\ndata_frame = {dict: N}\n|-- 'problem' = {list: N}\n| |-- 0 = {str}\n: :\n|-- 'dimensions' = {list: N}\n| |-- 0 = {int}\n: :\n|-- 'results' = {list: N}\n| |-- 0 = {dict: 6}\n| | |-- 'iteration' = {list: M} \n| | | |-- 0 = {int}\n: : : :\n| | |-- 'time' = {list: M}\n| | | |-- 0 = {float}\n: : : :\n| | |-- 'performance' = {list: M}\n| | | |-- 0 = {float}\n: : : :\n| | |-- 'encoded_solution' = {list: M}\n| | | |-- 0 = {int}\n: : : :\n| | |-- 'solution' = {list: M}\n| | | |-- 0 = {list: C}\n| | | | |-- 0 = {list: 3}\n| | | | | |-- search_operator_structure\n: : : : : :\n| | |-- 'details' = {list: M}\n| | | |-- 0 = {dict: 4}\n| | | | |-- 'fitness' = {list: R}\n| | | | | |-- 0 = {float}\n: : : : : :\n| | | | |-- 'positions' = {list: R}\n| | | | | |-- 0 = {list: D}\n| | | | | | |-- 0 = {float}\n: : : : : : :\n| | | | |-- 'historical' = {list: R}\n| | | | | |-- 0 = {dict: 5}\n| | | | | | |-- 'fitness' = {list: I}\n| | | | | | | |-- 0 = {float}\n: : : : : : : :\n| | | | | | |-- 'positions' = {list: I}\n| | | | | | | |-- 0 = {list: D}\n| | | | | | | | |-- 0 = {float}\n: : : : : : : : :\n| | | | | | |-- 'centroid' = {list: I}\n| | | | | | | |-- 0 = {list: D}\n| | | | | | | | |-- 0 = {float}\n: : : : : : : : :\n| | | | | | |-- 'radius' = {list: I}\n| | | | | | | |-- 0 = {float}\n: : : : : : : :\n| | | | | | |-- 'stagnation' = {list: I}\n| | | | | | | |-- 0 = {int}\n: : : : : : : :\n| | | | |-- 'statistics' = {dict: 10}\n| | | | | |-- 'nob' = {int}\n| | | | | |-- 'Min' = {float}\n| | | | | |-- 'Max' = {float}\n| | | | | |-- 'Avg' = {float}\n| | | | | |-- 'Std' = {float}\n| | | | | |-- 'Skw' = {float}\n| | | | | |-- 'Kur' = {float}\n| | | | | |-- 'IQR' = {float}\n| | | | | |-- 'Med' = {float}\n| | | | | |-- 'MAD' = {float}\n: : : : : :\n```\nwhere:\n- ```N``` is the number of files within data_files folder\n- ```M``` is the number of hyper-heuristic iterations (metaheuristic candidates)\n- ```C``` is the number of search operators in the metaheuristic (cardinality)\n- ```P``` is the number of control parameters for each search operator\n- ```R``` is the number of repetitions performed for each metaheuristic candidate\n- ```D``` is the dimensionality of the problem tackled by the metaheuristic candidate\n- ```I``` is the number of iterations performed by the metaheuristic candidate\n- ```search_operator_structure``` corresponds to ```[operator_name = {str}, control_parameters = {dict: P}, selector = {str}]```\n</p>\n</details>\n\n## \ud83c\udfd7\ufe0f Work-in-Progress\n\nThe following modules are available, but they may do not work. They are currently under developing.\n\n### \ud83c\udf21\ufe0f Characterisation\n\nThis module intends to provide metrics for characterising the benchmark functions.\n\nSource: [``characterisation.py``](customhys/characterisation.py)\n\n### \ud83d\udcca Visualisation\n\nThis module intends to provide several tools for plotting results from the experiments.\n\nSource: [``visualisation.py``](customhys/visualisation.py)\n\n## Sponsors\n\n<a href=\"https://tec.mx/en\" target=\"_blank\"><img src=\"https://github.com/jcrvz/customhys/raw/master/docfiles/logoTEC_full.png\" width=\"250\"></a>\n<a href=\"http://www.cas.cn/\" target=\"_blank\"><img src=\"https://github.com/jcrvz/customhys/raw/master/docfiles/cas_logo.png\" width=\"250\"></a>\n<a href=\"https://www.gob.mx/conacyt\" target=\"_blank\"><img src=\"https://github.com/jcrvz/customhys/raw/master/docfiles/conacyt-logo.png\" width=\"250\"></a>\n\n## References\n\n1. [J. M. Cruz-Duarte, I. Amaya, J. C. Ortiz-Bayliss, H. Terashima-Mar\u00edn, and Y. Shi, CUSTOMHyS: Customising Optimisation Metaheuristics via Hyper-heuristic Search, SoftwareX, vol. 12, p. 100628, 2020.](https://www.sciencedirect.com/science/article/pii/S2352711020303411)\n1. [J. M. Cruz-Duarte, I. Amaya, J. C. Ortiz-Bayliss, S. E. Conant-Pablos, H. Terashima-Mar\u00edn, H., and Y. Shi. _Hyper-Heuristics to Customise Metaheuristics for Continuous Optimisation_, *Swarm and Evolutionary Computation*, 100935.](https://doi.org/10.1016/j.swevo.2021.100935)\n1. [J. M. Cruz-Duarte, I. Amaya, J. C. Ortiz-Bayliss, S. E. Connat-Pablos, and H. Terashima-Mar\u00edn, A Primary Study on Hyper-Heuristics to Customise Metaheuristics for Continuous Optimisation. CEC'2020.](docfiles/SearchOperators_CEC.pdf)\n1. [J. M. Cruz-Duarte, J. C. Ortiz-Bayliss, I. Amaya, Y. Shi, H. Terashima-Mar\u00edn, and N. Pillay, Towards a Generalised Metaheuristic Model for Continuous Optimisation Problems, Mathematics, vol. 8, no. 11, p. 2046, Nov. 2020.](https://www.mdpi.com/2227-7390/8/11/2046)\n1. [J. M. Cruz-Duarte, J. C. Ortiz-Bayliss, I. Amaya, and N. Pillay, _Global Optimisation through Hyper-Heuristics: Unfolding Population-Based Metaheuristics_, *Appl. Sci.*, vol. 11, no. 12, p. 5620, 2021.](http://dx.doi.org/10.3390/app11125620)\n1. [J. M. Cruz-Duarte, I. Amaya, J. C. Ortiz-Bayliss, N. Pillay. Automated Design of Unfolded Metaheuristics and the Effect of Population Size. 2021 IEEE Congress on Evolutionary Computation (CEC), 1155\u20131162, 2021.](https://doi.org/10.1109/CEC45853.2021.9504879)\n1. [J. M. Tapia-Avitia, J. M. Cruz-Duarte, I. Amaya, J. C. Ortiz-Bayliss, H. Terashima-Marin, and N. Pillay. _A Primary Study on Hyper-Heuristics Powered by Artificial Neural Networks for Customising Population-based Metaheuristics in Continuous Optimisation Problems_, 2022 IEEE Congress on Evolutionary Computation (CEC), 2022.](https://doi.org/10.1109/CEC55065.2022.9870275)\n1. [J. M. Cruz-Duarte, I. Amaya, J. C. Ortiz-Bayliss, N. Pillay. _A Transfer Learning Hyper-heuristic Approach for Automatic Tailoring of Unfolded Population-based Metaheuristics_, 2022 IEEE Congress on Evolutionary Computation (CEC), 2022.](https://doi.org/10.1109/CEC55065.2022.9870426)\n",
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"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [],
"lcname": "customhys"
}
JSON
