• Corpus ID: 116922438

A multiobjective optimization approach to statistical mechanics

@article{Seoane2013AMO,
  title={A multiobjective optimization approach to statistical mechanics},
  author={Lu{\'i}s F. Seoane and Ricard V. Sol'e},
  journal={arXiv: Statistical Mechanics},
  year={2013}
}
Optimization problems have been the subject of statistical physics approximations. A specially relevant and general scenario is provided by optimization methods considering tradeoffs between cost and efficiency, where optimal solutions involve a compromise between both. The theory of Pareto (or multi objective) optimization provides a general framework to explore these problems and find the space of possible solutions compatible with the underlying tradeoffs, known as the {\em Pareto front… 

Figures from this paper

Multiobjective Optimization and Phase Transitions
TLDR
A robust framework is summarized that accounts for phase transitions located through SOO techniques and indicates what MOO features resolutely lead to phase transitions, suggesting that the similarities between transitions in MOO-SOO and Statistical Mechanics go beyond mere coincidence.
Systems poised to criticality through Pareto selective forces
Pareto selective forces optimize several targets at the same time, instead of single fitness functions. Systems subjected to these forces evolve towards their Pareto front, a geometrical object akin
Fate of Duplicated Neural Structures
TLDR
This work focuses on the fate of duplicated neural circuits, and derives phase diagrams and (phase-like) transitions between single and duplicated circuits, which constrain evolutionary paths to complex cognition.
Aging, computation, and the evolution of neural regeneration processes
TLDR
The organism’s lifespan and the external damage rates are found to act as an evolutionary pressure that improve the exploration of the space of solutions and poses tighter optimality conditions.
Criticality in Pareto Optimal Grammars?
TLDR
This work presents a probabilistic procedure for estimating the total number of components in a complex system and its architecture and describes the “building blocks” of the system.
Ageing, computation and the evolution of neural regeneration processes
TLDR
This work demands that digital organisms equipped with neural networks solve a computational task reliably over an extended lifespan, and investigates the simultaneous minimization of both these costs and the computational error.
Evolutionary aspects of reservoir computing
  • L. F. Seoane
  • Computer Science
    Philosophical Transactions of the Royal Society B
  • 2019
TLDR
A conceptual morphospace is introduced that would map computational selective pressures that could select for or against RC and other computing paradigms to propose a solid research line that brings together computation and evolution with RC as test model of the proposed hypotheses.
The morphospace of language networks
TLDR
A detailed analysis of network properties on a generic model of a communication code reveals a rather complex and heterogeneous morphospace of language graphs and indicates a surprisingly simple structure in human language unless particles with the ability of naming any other concept are introduced in the vocabulary.
Critical behaviour in charging of electric vehicles
The increasing penetration of electric vehicles over the coming decades, taken together with the high cost to upgrade local distribution networks and consumer demand for home charging, suggest that
Congestion control in charging of electric vehicles
TLDR
This work model the max-flow and proportional fairness protocols for the control of congestion caused by a fleet of vehicles charging on distribution networks, and finds that the critical arrival rate is indistinguishable between the two protocols.

References

SHOWING 1-10 OF 46 REFERENCES
Evolutionary algorithms for multiobjective optimization: methods and applications
TLDR
The basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective and the focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms.
An Overview of Evolutionary Algorithms in Multiobjective Optimization
TLDR
Current multiobjective evolutionary approaches are discussed, ranging from the conventional analytical aggregation of the different objectives into a single function to a number of population-based approaches and the more recent ranking schemes based on the definition of Pareto optimality.
Optimization of multiple criteria: Pareto efficiency and fast heuristics should be more popular than they are
TLDR
A recently published collective volume reports the results of a study group of the Berlin-Brandenburg Academy of Sciences on Structure Evolution and Innovation, which among other things is dealing with the problem of optimization in case of multiple goals and how humans can successfully manage such complex situations.
Evolutionary multi-objective optimization: a historical view of the field
  • C. Coello
  • Computer Science
    IEEE Comput. Intell. Mag.
  • 2006
This article provides a general overview of the field now known as "evolutionary multi-objective optimization," which refers to the use of evolutionary algorithms to solve problems with two or more
Evolutionary Trade-Offs, Pareto Optimality, and the Geometry of Phenotype Space
TLDR
It is found that best–trade-off phenotypes are weighted averages of archetypes—phenotypes specialized for single tasks, which could explain linear trait correlations, allometric relationships, as well as bacterial gene-expression patterns.
Physical aspects of evolutionary optimization and adaptation.
TLDR
A model of an objective function based on polynucleotide folding is used to investigate the dynamics of evolutionary adaptation in finite populations and represents a realistic example of a highly ``rugged landscape.
Parallel predator–prey interaction for evolutionary multi-objective optimization
TLDR
A gradient-based local search mechanism is integrated to exploit problem independent parallelization and hybridize the model in order to achieve faster convergence and solution stability and achieve a good approximation and unfold further parallelization potential for the model.
Evolutionary Tradeoffs between Economy and Effectiveness in Biological Homeostasis Systems
TLDR
This work used a simple and general model for regulation, known as integral feedback, and showed that best-compromise systems have particular combinations of biochemical parameters that control the response rate and basal level, and found that the optimal systems fall on a curve in parameter space.
...
...