Multi-objective optimization in rule-based design space exploration

Abstract

Design space exploration (DSE) aims to find optimal design candidates of a domain with respect to different objectives where design candidates are constrained by complex structural and numerical restrictions. 14,18] aims to find such candidates that are reachable from an initial model by applying a sequence of exploration rules. Solving a rule-based DSE problem is a difficult challenge due to the inherently dynamic nature of the problem.In the current paper, we propose to integrate multi-objective optimization techniques by using Non-dominated Sorting Genetic Algorithms (NSGA) to drive rule-based design space exploration. For this purpose, finite populations of the most promising design candidates are maintained wrt. different optimization criteria. In our context, individuals of a generation are defined as a sequence of rule applications leading from an initial model to a candidate model. Populations evolve by mutation and crossover operations which manipulate (change, extend or combine) rule execution sequences to yield new individuals.Our multi-objective optimization approach for rule-based DSE is domain independent and it is automated by tooling built on the Eclipse framework. The main added value is to seamlessly lift multi-objective optimization techniques to the exploration process preserving both domain independence and a high-level of abstraction. Design candidates will still be represented as models and the evolution of these models as rule execution sequences. Constraints are captured by model queries while objectives can be derived both from models or rule applications. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.

Evaluation of Optimization Strategies for Incremental Graph Queries

“…In the future, we plan to implement a graph query optimizer utilizing design space exploration (DSE) techniques, e.g. the Viatra-DSE [1] engine, which will feature both heuristic optimization techniques (e.g. pushing selection operators closer to leaf nodes) and cost-based query plans transformations.…”

Towards Model-Based Optimisation: Using Domain Knowledge Explicitly

“…Abdeen et al [13] propose encoding different transformation sequences as genomes so that they become amenable to search with a genetic algorithm. Their approach is based on Viatra.…”

“…The paper provides some good discussion of the issue of invalid individuals: these may be the result of crossover or mutation, making transformation chains invalid or causing them to create invalid models. [13] uses a specific ranking mechanism to handle these cases.…”

“…Abdeen et al [13] give a good discussion of these issues in the context of genetic optimisation of model-transformation chains, where they use repair as well as customised ranking rules. Similar techniques could be applied to model-based optimisation, too.…”

“…Model-driven engineering (MDE) offers good techniques for capturing complex domains including their structural constraints by using meta-models. As a result, there has recently been increased interest in combining the advantages of SBSE and MDE [6,7,8,9,10,11,12,13,14,15].…”