Pyhon code and raw data for our proposed algorithm (GANMA)

Description of Source Code and Raw Data

Overview: The provided source code and raw data files are designed for evaluating the performance of a proposed algorithm using 15 widely recognized benchmark functions. These functions are critical for assessing the algorithm's efficiency, robustness, and effectiveness in optimization tasks. The evaluation is conducted across three different dimensions: 10, 20, and 30, providing a comprehensive analysis of the algorithm's capability to handle varying complexities.

Components:

1. Source Code:

The source code is implemented to execute the proposed algorithm on the benchmark functions. It includes modules for initializing populations, applying genetic operations (selection, crossover, mutation), and measuring performance metrics such as fitness value, convergence rate, and computational time.

The code is adaptable for different dimensional settings (10, 20, 30 dimensions) and can be easily modified to adjust parameters such as population size, iteration count, and genetic operators' specifics.

The algorithm is tested against a suite of 15 benchmark functions, each representing a unique challenge in the optimization landscape, including unimodal, multimodal, separable, and non-separable functions.

2. Raw Data:

The raw data consists of the results generated by running the proposed algorithm on each benchmark function across the three dimensional settings (10, 20, and 30 dimensions).

Data includes multiple runs to ensure statistical significance, capturing metrics like the best and average fitness values, standard deviation, and convergence behavior over the iterations.

This data is crucial for performing comparative analysis, highlighting the strengths and weaknesses of the proposed algorithm relative to existing methods.

Benchmark Functions:

· The 15 benchmark functions include a mix of well-known test cases such as Sphere, Rosenbrock, Rastrigin, Ackley, and others. Each function is crafted to test different aspects of optimization algorithms, from dealing with high-dimensional search spaces to escaping local optima.

· The functions are provided in the standard mathematical form, and the source code includes implementation details for each.

Purpose:

· The primary goal of this package is to validate the effectiveness of the proposed algorithm against standard benchmarks in the field. The source code enables reproducibility of results, while the raw data serves as a baseline for further research and comparison with other optimization techniques.

Usage:

· Researchers can use the provided source code to replicate the experiments or adapt the algorithm for other benchmark functions or dimensional settings.

· The raw data can be analyzed using statistical tools to derive insights into the algorithm's performance across different scenarios.