Welcome to genetic programming, where the forces of nature are used to automatically evolve computer programs. We give a flavour of where GP has been successfully applied (it is far too wide an area to cover everything) and interesting current and future research but start with a tutorial of how to get started and finish with common pitfalls to avoid.

Much of evolutionary computation was inspired by Mendelian genetics. But modern genetics has since advanced considerably, revealing that genes are not simply parameter settings, but interactive cogs in a complex chemical machine. At the same time, an increasing number of evolutionary computation domains are evolving non-parameterized mechanisms such as neural networks or symbolic computer progr...

Two prominent genetic programming approaches are the graph-based Cartesian Genetic Programming (CGP) and Linear Genetic Programming (LGP). Recently, a formal algorithm for constructing a directed acyclic graph (DAG) from a classical LGP instruction sequence has been established. Given graphbased LGP and traditional CGP, this paper investigates the similarities and differences between the two im...

This thesis introduces a new approach to program representation in genetic programming in which interactions between program components are expressed in terms of a component’s behaviour rather through its relative position within a representation or through other non-behavioural systems of reference. This approach has the advantage that a component’s behaviour is expressed in a way that is inde...

although several papers have studied no-idle scheduling problems, they all focus on flow shops, assuming one processor at each working stage. but, companies commonly extend to hybrid flow shops by duplicating machines in parallel in stages. this paper considers the problem of scheduling no-idle hybrid flow shops. a mixed integer linear programming model is first developed to mathematically form...

resource limitation in zero time may cause to some profitable projects not to be selected in project selection problem, thus simultaneous project portfolio selection and scheduling problem has received significant attention. in this study, budget, investment costs and earnings are considered to be stochastic. the objectives are maximizing net present values of selected projects and minimizing v...