Parameter-Driven Generation of Evaluation Program for a Neuroevolution Algorithm on a Binary Multiplexer Example

Abstract

Context. The problem of automated development of evaluation programs for the neuroevolution of augmenting topologies. Neuroevolution algorithms apply mechanisms of mutation, recombination, and selection to find neural networks with behavior that satisfies the conditions of a certain formally defined problem. An example of such a problem is finding a neural network that implements a certain digital logic.
 Objective. The goal of the work is the automated design and generation of an evaluation program for a sample neuroevolution problem (binary multiplexer).
 Method. The methods and tools of Glushkov’s algebra of algorithms and hyperscheme algebra are applied for the parameterdriven generation of a neuroevolution evaluation program for a binary multiplexer. Glushkov’s algebra is the basis of the algorithmic language intended for multilevel structural design and documentation of sequential and parallel algorithms and programs in a form close to a natural language. Hyperschemes are high-level parameterized specifications intended for solving a certain class of problems. Setting parameter values and subsequent interpretation of hyperschemes allows obtaining algorithms adapted to specific conditions of their use.
 Results. The facilities of hyperschemes were implemented in the developed integrated toolkit for the automated design and synthesis of programs. Based on algorithm schemes, the system generates programs in a target programming language. The advantage of the system is the possibility of describing algorithm schemes in a natural-linguistic form. An experiment was conducted consisting in the execution of the generated program for the problem of evaluating a binary multiplexer on a distributed cloud platform. The multiplexer example is included in SharpNEAT, an open-source framework that implements the genetic neuroevolution algorithm NEAT for the .NET platform. The parallel distributed implementation of the SharpNEAT was proposed in the previous work of the authors.
 Conclusions. The conducted experiments demonstrated the possibility of the developed distributed system to perform evaluations on 64 cloud clients-executors and obtain an increase in 60–100% of the maximum capabilities of a single-processor local implementation.