Abstract. Prior program representations for genetic programming that incorporated features of modern programming languages solved harder problems than earlier representations, but required more complex genetic operators. We develop the idea of using combinator expressions as a program representation for genetic programming. This representation makes it possible to evolve programs with a variety of programming language constructs using simple genetic operators. We investigate the effort required to evolve combinator-expression solutions to several problems: linear regression, even parity on N inputs, and implementation of the stack and queue data structures. Genetic programming with combinator expressions compares favorably to prior approaches, namely the works

This paper presents a model that simulates a selfassembly process for software components. Initial investigations on the Automated Self-Assembly Programming Paradigm (ASAP ) is presented whereby software components are treated as a gas' molecules and their interactions, within a confined area with specific temperature and pressure constraints, give rise to a variety of program architectures.