scholarly journals Schemata Evolution and Building Blocks

1999 ◽  
Vol 7 (2) ◽  
pp. 109-124 ◽  
Author(s):  
Chris Stephens ◽  
Henri Waelbroeck
Keyword(s):  
Genetic Algorithms ◽  
Evolution Equation ◽  
Building Block ◽  
Building Blocks ◽  
Effective Fitness ◽  
Over Time ◽  
Low Order

In the light of a recently derived evolution equation for genetic algorithms we consider the schema theorem and the building block hypothesis. We derive a schema theorem based on the concept of effective fitness showing that schemata of higher than average effective fitness receive an exponentially increasing number of trials over time. The equation makes manifest the content of the building block hypothesis showing how fit schemata are constructed from fit sub-schemata. However, we show that, generically, there is no preference for short, low-order schemata. In the case where schema reconstruction is favored over schema destruction, large schemata tend to be favored. As a corollary of the evolution equation we prove Geiringer's theorem.

Creating Successful Portals with a Design Framework

2009 ◽  
Vol 1 (4) ◽  
pp. 63-75 ◽  
Author(s):  
Joe Lamantia
Keyword(s):  
Building Block ◽  
Large Scale ◽  
Building Blocks ◽  
Design Principles ◽  
Design Framework ◽  
Block System ◽  
Business Information ◽  
Social Settings ◽  
The Creation ◽  
Over Time

Portal designers and managers face the difficulties of creating effective information architectures for portals, dashboards, and tile-based platforms for delivering business information and functionality using only flat portlets. This article introduces the idea of a system of standardized building blocks that can simplifies portal design and management, and effectively support growth in content, functionality, and users over time. In enterprise and other large scale social settings, using standardized components allows for the creation of a library of tiles that can be shared across communities of users. It then outlines the design principles underlying the building block system, and the simple guidelines for combining blocks together to create any type of tile-based environment.

Creating Successful Portals with a Design Framework

2011 ◽  
pp. 246-256
Author(s):  
Joe Lamantia
Keyword(s):  
Building Block ◽  
Large Scale ◽  
Building Blocks ◽  
Design Principles ◽  
Design Framework ◽  
Block System ◽  
Social Settings ◽  
The Creation ◽  
Over Time

Portal practitioners face the difficulties of creating effective information architectures for portals, dashboards, and tile-based information environments using only flat portlets. This article introduces the idea of a system of standardized building blocks that can effectively support growth in content, functionality, and users over time. In enterprise and other large scale social settings, using standardized components allows for the creation of a library of tiles that can be shared across communities of users. It then outlines the design principles underlying the building block system, and the simple guidelines for combining blocks together to create any type of tile-based environment.

Designing Genetic Algorithms to Solve GIS Problems

2001 ◽  
Author(s):  
Dirk Thierens ◽  
Mark De Berg
Keyword(s):  
Genetic Algorithms ◽  
Building Block ◽  
Domain Knowledge ◽  
Geographical Information Systems ◽  
Optimal Solution ◽  
Building Blocks ◽  
Geographical Information ◽  
Specific Class ◽  
Map Labeling ◽  
Linkage Learning

What makes a problem hard for a genetic algorithm (GA)? How does one need to design a GA to solve a problem satisfactorily? How does the designer include domain knowledge in the GA? When is a GA suitable to use for solving a problem? These are all legitimate questions. This chapter will offer a view on genetic algorithms that stresses the role of the so-called linkage. Linkage relates to the fact that between the variables of the solution dependencies exist that cause a need to treat those variables as one “block,” since the best setting of each individual variable can only be determined by looking at the other variables as well. The genes that represent these variables will then have to be transferred together. When these genes are set to their optimal values, they constitute a building block. Building blocks will be transferred as a whole during recombination and the building blocks of all the genes make up the optimal solution. As will become apparent, knowing the linkage of a building block is a big advantage and will allow one to design efficient GAs. Sadly, in the majority of problems, the linkage is unknown. This observation has given rise to a lot of development in linkage learning algorithms (for an example, see Kargupta 1996). However, there is a specific class of problems that allows for relatively easy determination of linkage: spatial problems. This is because in these problems, the linkage is geometrically defined. We will focus in this chapter on certain hard problems that arise in the context of geographical information systems and for which the linkage can be easily found. Specifically, we will fully detail the design of a GA for the problem of map labeling, which is an important problem in automated cartography. The map labeling problem for point features is to find a placement for the labels of a set of points such that the number of labels that do not intersect other labels is maximized.

Creating Successful Portals with a Design Framework

2010 ◽  
pp. 167-178
Author(s):  
Joe Lamantia
Keyword(s):  
Building Block ◽  
Large Scale ◽  
Building Blocks ◽  
Design Principles ◽  
Design Framework ◽  
Block System ◽  
Business Information ◽  
Social Settings ◽  
The Creation ◽  
Over Time

Portal designers and managers face the difficulties of creating effective information architectures for portals, dashboards, and tile-based platforms for delivering business information and functionality using only flat portlets. This article introduces the idea of a system of standardized building blocks that can simplifies portal design and management, and effectively support growth in content, functionality, and users over time. In enterprise and other large scale social settings, using standardized components allows for the creation of a library of tiles that can be shared across communities of users. It then outlines the design principles underlying the building block system, and the simple guidelines for combining blocks together to create any type of tile-based environment.

Scalability Problems of Simple Genetic Algorithms

1999 ◽  
Vol 7 (4) ◽  
pp. 331-352 ◽  
Author(s):  
Dirk Thierens
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Computation ◽  
Building Block ◽  
Building Blocks ◽  
Research Topic ◽  
Problem Size ◽  
Clear Insight ◽  
Simple Genetic Algorithms ◽  
Algorithmic Design

Scalable evolutionary computation has. become an intensively studied research topic in recent years. The issue of scalability is predominant in any field of algorithmic design, but it became particularly relevant for the design of competent genetic algorithms once the scalability problems of simple genetic algorithms were understood. Here we present some of the work that has aided in getting a clear insight in the scalability problems of simple genetic algorithms. Particularly, we discuss the important issue of building block mixing. We show how the need for mixing places a boundary in the GA parameter space that, together with the boundary from the schema theorem, delimits the region where the GA converges reliably to the optimum in problems of bounded difficulty. This region shrinks rapidly with increasing problem size unless the building blocks are tightly linked in the problem coding structure. In addition, we look at how straightforward extensions of the simple genetic algorithm—namely elitism, niching, and restricted mating are not significantly improving the scalability problems.

A Comparison of the Fixed and Floating Building Block Representation in the Genetic Algorithm

1996 ◽  
Vol 4 (2) ◽  
pp. 169-193 ◽  
Author(s):  
Annie S. Wu ◽  
Robert K. Lindsay
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Building Block ◽  
Deoxyribonucleic Acid ◽  
Evolutionary Process ◽  
Building Blocks ◽  
Problem Representation ◽  
Parallel Search ◽  
Diverse Population ◽  
Significant Difference

This article compares the traditional, fixed problem representation style of a genetic algorithm (GA) with a new floating representation in which the building blocks of a problem are not fixed at specific locations on the individuals of the population. In addition, the effects of noncoding segments on both of these representations is studied. Noncoding segments are a computational model of noncoding deoxyribonucleic acid, and floating building blocks mimic the location independence of genes. The fact that these structures are prevalent in natural genetic systems suggests that they may provide some advantages to the evolutionary process. Our results show that there is a significant difference in how GAs solve a problem in the fixed and floating representations. Genetic algorithms are able to maintain a more diverse population with the floating representation. The combination of noncoding segments and floating building blocks appears to encourage a GA to take advantage of its parallel search and recombination abilities.

scholarly journals How Crossover Speeds up Building Block Assembly in Genetic Algorithms

2017 ◽  
Vol 25 (2) ◽  
pp. 237-274 ◽  
Author(s):  
Dirk Sudholt
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Building Block ◽  
Broad Class ◽  
Statistical Tests ◽  
Building Blocks ◽  
Fundamental Question ◽  
Royal Road ◽  
Crossover Experiments

We reinvestigate a fundamental question: How effective is crossover in genetic algorithms in combining building blocks of good solutions? Although this has been discussed controversially for decades, we are still lacking a rigorous and intuitive answer. We provide such answers for royal road functions and OneMax, where every bit is a building block. For the latter, we show that using crossover makes every ([Formula: see text]+[Formula: see text]) genetic algorithm at least twice as fast as the fastest evolutionary algorithm using only standard bit mutation, up to small-order terms and for moderate [Formula: see text] and [Formula: see text]. Crossover is beneficial because it can capitalize on mutations that have both beneficial and disruptive effects on building blocks: crossover is able to repair the disruptive effects of mutation in later generations. Compared to mutation-based evolutionary algorithms, this makes multibit mutations more useful. Introducing crossover changes the optimal mutation rate on OneMax from [Formula: see text] to [Formula: see text]. This holds both for uniform crossover and k-point crossover. Experiments and statistical tests confirm that our findings apply to a broad class of building block functions.

Emotional Variability Predicts Tiredness in Daily Life

2016 ◽  
Vol 37 (3) ◽  
pp. 181-193 ◽  
Author(s):  
Aire Mill ◽  
Anu Realo ◽  
Jüri Allik
Keyword(s):  
Sampling Method ◽  
Daily Life ◽  
Emotional Experience ◽  
Experience Sampling ◽  
Experience Sampling Method ◽  
Building Blocks ◽  
Intraindividual Variability ◽  
Unique Role ◽  
Over Time

Abstract. Intraindividual variability, along with the more frequently studied between-person variability, has been argued to be one of the basic building blocks of emotional experience. The aim of the current study is to examine whether intraindividual variability in affect predicts tiredness in daily life. Intraindividual variability in affect was studied with the experience sampling method in a group of 110 participants (aged between 19 and 84 years) during 14 consecutive days on seven randomly determined occasions per day. The results suggest that affect variability is a stable construct over time and situations. Our findings also demonstrate that intraindividual variability in affect has a unique role in predicting increased levels of tiredness at the momentary level as well at the level of individuals.

Thienopyrrolocarbazoles: New Building Blocks for Functional Organic Materials

2019 ◽  
Author(s):  
Dorian Bader ◽  
Johannes Fröhlich ◽  
Paul Kautny
Keyword(s):  
Building Block ◽  
Organic Materials ◽  
Building Blocks ◽  
Molecular Properties ◽  
The Family ◽  
Facile Preparation

The facile preparation of three regioisomeric thienopyrrolocarbazoles applying a convenient C-H activation approach is presented. Derived from indolo[3,2,1-<i>jk</i>]carbazole, the incorporation of thiophene into the triarylamine framework significantly impacted the molecular properties of the parent scaffold. The developed thienopyrrolocarbazoles enrich the family of triarylamine donors and constitute a novel building block for functional organic materials.

Thienopyrrolocarbazoles: New Building Blocks for Functional Organic Materials

2019 ◽  
Author(s):  
Dorian Bader ◽  
Johannes Fröhlich ◽  
Paul Kautny
Keyword(s):  
Building Block ◽  
Organic Materials ◽  
Building Blocks ◽  
Molecular Properties ◽  
The Family ◽  
Facile Preparation

The facile preparation of three regioisomeric thienopyrrolocarbazoles applying a convenient C-H activation approach is presented. Derived from indolo[3,2,1-<i>jk</i>]carbazole, the incorporation of thiophene into the triarylamine framework significantly impacted the molecular properties of the parent scaffold. The developed thienopyrrolocarbazoles enrich the family of triarylamine donors and constitute a novel building block for functional organic materials.

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