3,174 research outputs found
Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games
The term Procedural Content Generation (PCG) refers to the (semi-)automatic
generation of game content by algorithmic means, and its methods are becoming
increasingly popular in game-oriented research and industry. A special class of
these methods, which is commonly known as search-based PCG, treats the given
task as an optimisation problem. Such problems are predominantly tackled by
evolutionary algorithms.
We will demonstrate in this paper that obtaining more information about the
defined optimisation problem can substantially improve our understanding of how
to approach the generation of content. To do so, we present and discuss three
efficient analysis tools, namely diagonal walks, the estimation of high-level
properties, as well as problem similarity measures. We discuss the purpose of
each of the considered methods in the context of PCG and provide guidelines for
the interpretation of the results received. This way we aim to provide methods
for the comparison of PCG approaches and eventually, increase the quality and
practicality of generated content in industry.Comment: 30 pages, 8 figures, accepted for publication in Applied Soft
Computin
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Searching for improvement
Engineering design can be thought of as a search for the best solutions to engineering problems. To perform an effective search, one must distinguish between competing designs and establish a measure of design quality, or fitness. To compare different designs, their features must be adequately described in a well-defined framework, which can mean separating the creative and analytical parts of the design process. By this we mean that a distinction is drawn between coming up with novel design concepts, or architectures, and the process of detailing or refining existing design architecture. In the case of a given design architecture, one can consider the set of all possible designs that could be created by varying its features. If it were possible to measure the fitness of all designs in this set, then one could identify a fitness landscape and search for the best possible solution for this design architecture. In this Chapter, the significance of the interactions between design features in defining the metaphorical fitness landscape is described. This highlights that the efficiency of a search algorithm is inextricably linked to the problem structure (and hence the landscape). Two approaches, namely, Genetic Algorithms (GA) and Robust Engineering Design (RED) are considered in some detail with reference to a case study on improving the design of cardiovascular stents
Benchmarking for Metaheuristic Black-Box Optimization: Perspectives and Open Challenges
Research on new optimization algorithms is often funded based on the
motivation that such algorithms might improve the capabilities to deal with
real-world and industrially relevant optimization challenges. Besides a huge
variety of different evolutionary and metaheuristic optimization algorithms,
also a large number of test problems and benchmark suites have been developed
and used for comparative assessments of algorithms, in the context of global,
continuous, and black-box optimization. For many of the commonly used synthetic
benchmark problems or artificial fitness landscapes, there are however, no
methods available, to relate the resulting algorithm performance assessments to
technologically relevant real-world optimization problems, or vice versa. Also,
from a theoretical perspective, many of the commonly used benchmark problems
and approaches have little to no generalization value. Based on a mini-review
of publications with critical comments, advice, and new approaches, this
communication aims to give a constructive perspective on several open
challenges and prospective research directions related to systematic and
generalizable benchmarking for black-box optimization
The application of modified adaptive landscapes to heuristic modelling of engine concept designs using sparse data
The automotive internal combustion engine industry operates in a sector that relies on high production volumes for economies of scale, and dedicated production equipment for efficiency of operations and control of quality, yet is subject to the vagaries of a dynamic marketplace, with the need for constant change. These circumstances place pressure on engine designs to be optimised at launch to be competitive and meet market needs, yet be adaptable to uncertain requirements for change over their production life. Engine designers therefore need concept configuration evaluation tools that can assess architectures for resilience to geometric change over the production life of the product. The problem of being resource efficient whilst having the capacity to adapt tochanging environments is one that has been addressed in nature. Natural systems have evolved strategies of satisficing conflicting requirements whilst being resource efficient. The theory of adaptive landscapes helps us to visualise the adaptive capacity of potential morphological forms. A concept attribute analysis methodology based on satisficing and adaptive landscapes has been developed and tested for application to engine concept design. The Plateau, Flooded Adaptive Landscape technique (PFAL),has been evaluated against exemplar engine life histories and shows merit in aiding the decision-making process for concept designers working with sparse data. The process lets the designer visualise the attribute map, enabling them to make better trade-off decisions and share these with non-expert stakeholders to gain their input in final concept choices
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Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions
Copyright © 2014 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling and Software Vol. 62 (2014), DOI: 10.1016/j.envsoft.2014.09.013The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts
Using Automated Algorithm Configuration for Parameter Control
Dynamic Algorithm Configuration (DAC) tackles the question of how to
automatically learn policies to control parameters of algorithms in a
data-driven fashion. This question has received considerable attention from the
evolutionary community in recent years. Having a good benchmark collection to
gain structural understanding on the effectiveness and limitations of different
solution methods for DAC is therefore strongly desirable. Following recent work
on proposing DAC benchmarks with well-understood theoretical properties and
ground truth information, in this work, we suggest as a new DAC benchmark the
controlling of the key parameter in the
~Genetic Algorithm for solving OneMax problems. We
conduct a study on how to solve the DAC problem via the use of (static)
automated algorithm configuration on the benchmark, and propose techniques to
significantly improve the performance of the approach. Our approach is able to
consistently outperform the default parameter control policy of the benchmark
derived from previous theoretical work on sufficiently large problem sizes. We
also present new findings on the landscape of the parameter-control search
policies and propose methods to compute stronger baselines for the benchmark
via numerical approximations of the true optimal policies.Comment: To appear in the Proc. of the ACM/SIGEVO Conference on Foundations of
Genetic Algorithms (FOGA XVII
On the entropy of protein families
Proteins are essential components of living systems, capable of performing a
huge variety of tasks at the molecular level, such as recognition, signalling,
copy, transport, ... The protein sequences realizing a given function may
largely vary across organisms, giving rise to a protein family. Here, we
estimate the entropy of those families based on different approaches, including
Hidden Markov Models used for protein databases and inferred statistical models
reproducing the low-order (1-and 2-point) statistics of multi-sequence
alignments. We also compute the entropic cost, that is, the loss in entropy
resulting from a constraint acting on the protein, such as the fixation of one
particular amino-acid on a specific site, and relate this notion to the escape
probability of the HIV virus. The case of lattice proteins, for which the
entropy can be computed exactly, allows us to provide another illustration of
the concept of cost, due to the competition of different folds. The relevance
of the entropy in relation to directed evolution experiments is stressed.Comment: to appear in Journal of Statistical Physic
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