10am One minutes silence held at the beginning of the workshop for our beloved colleague Professor David Notkin who died yesterday. Our thoughts are with his family and friends at this very sad time.
Our first speaker this morning is Juergen Branke from Warwick Business School “Optimization in dynamic environments”
Evolutionary algorithms have grown into a mature field of research in optimization and have proven to be effective and robust problem solvers for a broad range of static real world optimization problems. Yet, since they are based on the principles of natural evolution and since natural evolution is a dynamic process in a changing environment they are also well suited to dynamic optimization problems.
With appropriate adjustments evolutionary computation is able to continuously adapt and is promising for applications where very few function evaluations are possible.
Next is Robert Feldt from Chalmers University of Technology, Sweden with ” Interactive and Adaptive Automated Testing Environments”
15 years ago Robert wanted to automatically generate program code, He failed but learnt a lot from that. He developed an IDE where you could automatically generate test cases.
Fitness guidance – f-Equalizer – being developed which can compare test case similarity.
– break –
Next is Michael Orlov from Department of Computer Science, Ben-Gurion University, Israel
Evolving unrestricted Java software with FINCH
FINCH is a methodology for evolving Java bytecode enabling the evolution of extant unrestricted Java programs or programs.
Michael wanted to evolve some Java source code and improve some existing Java software.
The team decided to use Java – JVM bytecode rather than parse trees.
They used bytecode because it is less fragile than source code but must be correct in order to run correctly so genetic operators are still delicate. Good genetic operators are needed to produce correct offspring.
Java Wilderness, Artificial Ant
They found that completely unrestricted Java programs can be evolved via bytecode and extant Java programs can be improved.
– lunch –
First up after lunch is David White from Computing Sciences, University of Glasgow, UK talking about “The Programming Game: an alternative to GP for Expression Search”
David asks how well do we understand GP genetic programming search and talks about an alternative program search method. Monte Carlo tree search achieves some amazing results in the game of Go. It works by looking at game trees.
Advantages of Monte Carlo tree search are concise solutions, the game tree is human readable and parallelisation.
Future work includes adapting Monte Carlo tree search for program search.
Our last speaker is Justyna Petke from CREST Centre, SSE Group, Department of Computer Science, UCL. She will be speaking about “Genetic improvement of software: a case study”
Genetic improvement programming aims to improve code. Code is changed by reusing lines of the original code and adding, deleting, mutation, crossover etc.
Findings of the study were that genetic improvement programming automatically improves system behaviour in selected cases.
-Workshop wrap up –
Professor Mark Harman, DAASE project PI, thanks our speakers and everyone for coming.
Our workshop is now finished. Many thanks to all of our speakers and session chairs and of course our audience. See you at #COW27 😃