Performance analysis of parallel master-slave Evolutionary strategies (μ,λ) model python implementation for CPU and GPGPU
Evolutionary strategies is a heuristic, guided-search based evolutionary algorithm, widely used as optimization technique for computationally intensive problems. Python is a high-level programming language known for code readability, reusability and the ease of use, making it preferable choice for quick and robust software development, although it is lacking in performance and concurrency area. Emerging technologies such as Anaconda Accelerate Python compiler attempt to combine Python's ease of use with both declarative and explicit parallelization and high performance in computationally intensive problems. In this paper an example of master - slave parallel Evolutionary strategy ES(μ,λ) implementation in Python is given, and its performance on CPU and GPU are analyzed.