The measured cost of conservative garbage collection
Software—Practice & Experience
Composing high-performance memory allocators
Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation
Efficient representations and abstractions for quantifying and exploiting data reference locality
Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Dynamic Storage Allocation: A Survey and Critical Review
IWMM '95 Proceedings of the International Workshop on Memory Management
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Systematic dynamic memory management design methodology for reduced memory footprint
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Optimization of dynamic memory managers for embedded systems using grammatical evolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Efficient system-level prototyping of power-aware dynamic memory managers for embedded systems
Integration, the VLSI Journal - Special issue: Low-power design techniques
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Simulation of High-Performance Memory Allocators
DSD '10 Proceedings of the 2010 13th Euromicro Conference on Digital System Design: Architectures, Methods and Tools
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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The dynamic memory manager (DMM) is a key element whose customization for a target application reports great benefits in terms of execution time, memory usage and energy consumption. Previous works presented algorithms to automatically obtain custom DMMs for a given application. Nevertheless, those approaches are based on grammatical evolution where the fitness is built as an aggregate objective function, which does not completely exploit the search space, returning the designer the DMM solution with best fitness. However, this approach may not find solutions that could fit in a concrete hardware platform due to a very low value of one of the objectives while the others remain high, which may represent a high fitness. In this work we present the first multi-objective optimization methodology applied to DMM optimization where the Pareto dominance is considered, thus providing the designer with a set of non-dominated DMM implementations on each optimization run. Our results show that the multi-objective optimization provides Pareto-optimal alternatives due to a better exploitation of the search space obtaining better hypervolume values than the aggregate objective function approach.