A typical machine learning development cycle maximizes performance during model
training and then minimizes the memory and area footprint of the trained model for …

Just over a decade ago, the first comprehensive review on the state of benchmarking in
Genetic Programming (GP) analyzed the mismatch between the problems that are used to …

Genetic algorithm is an exploratory method inspired by Darwin's theory of natural evolution.
This algorithm reflects the natural selection process in which suitable individuals are …

Scalability problems have hindered the progress of Evolvable Hardware in tackling complex
circuits. The two key issues are the amount of testing (for example, a 64-bit *× 64-bit add …

Logic synthesis tools face tough challenges when providing algorithms for synthesizing
circuits with increased inputs and complexity. Traditional approaches for logic synthesis …

The paper addresses the problem of the automatic design of sequential systems. For a
complete description of the operation of the sequential system, a table of states or another …

An essential characteristic of brains in intelligent organisms is their spatial organization, in
which different parts of the brain are responsible for solving different classes of problems …

The optimization of the number of transistors of combinational logic circuits can lead to faster
and cheaper electronic devices, but it is an NP-complete problem. There are deterministic …

Unconventional functions, including activation functions and power functions, are extremely
hard‐to‐realize primarily due to the difficulty in arriving at the hierarchical design. The …

The search performance of Cartesian Genetic Programming (CGP) relies to a large extent
on the sole use of genotypic point mutation in combination with extremely large redundant …