Genotypic Variation in Constitutive and Induced Resistance in Grapes against Spider Mite (Acari: Tetranychidae) Herbivores

We examined genotypic variation in constitutive and induced resistance in grapes against Willamette spider mites, Eotetranychus willametti Ewing, and Pacific spider mites, Tetranychus pacificus McGregor, 2 common species of tetranychid mites found in California vineyards. We found evidence that early-season injury by Pacific mites induced resistance against subsequent Willamette mite populations but early-season injury by Willamette mites did not induce resistance against subsequent Willamette mite populations. Significant levels of induction were detected for several cultivars of the Old World species Vitis vinifera L. as well as the North American species V. calif arnica Bentham. Phylogenetic relationships among grape genotypes explained little of the variation we observed in induced resistance. Phylogenetic relatedness among grapes did help explain patterns of constitutive resistance for Pacific mites; cultivars of V. vinifera L. tended to be susceptible, whereas North American species were resistant. Wi11amette mites, however, performed well on some Old World cultivars and 2 North American species of Vitis that are native to California. We did not find any strong evidence of a negative correlation between constitutive resistance and strength of induction for these grape genotypes. Our results show that several factors contribute to variation in constitutive and induced resistance in grapes against these 2 species of spider mites, including grape genotype, previous history of mite injury (induction), the species of mite causing previous injury, and to some extent, phylogenetic relatedness among grapes. We also suspect that mite genotype has important influence

Evidence on the Validity of Management Education

The authors feel that more attention should be given to the empirical validation of management education. In order to determine what effect a college degree and the academic major have on promotability, 3,202 marketing personnel of a major petroleum corporation were analyzed. What effect does a college education have on executive success? Does the major area of study make any difference? Does any kind of management education or development yield tangible returns to an employing organization? In other words, have management formal education and development been empirically validated? Many organizations are seriously beginning to ask these questions. The current body of management knowledge has not given a satisfactory answer

Life on the Edge: Characterising the Edges of Mutually Non-dominating Sets

Copyright © 2014 Massachusetts Institute of TechnologyMulti-objective optimisation yields an estimated Pareto front of mutually nondominating solutions, but with more than three objectives understanding the relationships between solutions is challenging. Natural solutions to use as landmarks are those lying near to the edges of the mutually non-dominating set. We propose four definitions of edge points for many-objective mutually non-dominating sets and examine the relations between them. The first defines edge points to be those that extend the range of the attainment surface. This is shown to be equivalent to finding points which are not dominated on projection onto subsets of the objectives. If the objectives are to be minimised, a further definition considers points which are not dominated under maximisation when projected onto objective subsets. A final definition looks for edges via alternative projections of the set. We examine the relations between these definitions and their efficacy in many dimensions for synthetic concave- and convex shaped sets, and on solutions to a prototypical many-objective optimisation problem, showing how they can reveal information about the structure of the estimated Pareto front. We show that the “controlling dominance area of solutions” modification of the dominance relation can be effectively used to locate edges and interior points of high-dimensional mutually non-dominating sets

Edges of Mutually Non-dominating Sets

Copyright © 2013 ACM. This is the accepted, peer-reviewed version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 15th annual conference on Genetic and Evolutionary Computation (GECCO ’13), pp. 607-614, http://dx.doi.org/10.1145/2463372.246345215th annual conference on Genetic and Evolutionary Computation (GECCO ’13), Amsterdam, The Netherlands, 6-10 July 2013Notes: Won the Best Paper Award in the EMO trackMulti-objective optimisation yields an estimated Pareto front of mutually non-dominating solutions, but with more than three objectives understanding the relationships between solutions is challenging. Natural solutions to use as landmarks are those lying near to the edges of the mutually non-dominating set. We propose four definitions of edge points for many-objective mutually non-dominating sets and examine the relations between them. The first defines edge points to be those that extend the range of the attainment surface. This is shown to be equivalent to finding points which are not dominated on projection onto subsets of the objectives. If the objectives are to be minimised, a further definition considers points which are not dominated under maximisation when projected onto objective subsets. A final definition looks for edges via alternative projections of the set. We examine the relations between these definitions and their efficacy for synthetic concave- and convex-shaped sets, and on solutions to a prototypical many-objective optimisation problem, showing how they can reveal information about the structure of the estimated Pareto front

Rank-based dimension reduction for many-criteria populations

Copyright © 2011 ACM13th annual conference on Genetic and Evolutionary Computation (GECCO '11), Dublin, Ireland, 12-16 July 2011Interpreting individuals described by a set of criteria can be a difficult task when the number of criteria is large. Such individuals can be ranked, for instance in terms of their average rank across criteria as well as by each distinct criterion. We therefore investigate criteria selection methods which aim to preserve the average rank of individuals but with fewer criteria. Our experiments show that these methods perform effectively, identifying and removing redundancies within the data, and that they are best incorporated into a multi-objective algorithm

Visualising Mutually Non-dominating Solution Sets in Many-objective Optimisation

Copyright © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.As many-objective optimization algorithms mature, the problem owner is faced with visualizing and understanding a set of mutually nondominating solutions in a high dimensional space. We review existing methods and present new techniques to address this problem. We address a common problem with the well-known heatmap visualization, since the often arbitrary ordering of rows and columns renders the heatmap unclear, by using spectral seriation to rearrange the solutions and objectives and thus enhance the clarity of the heatmap. A multiobjective evolutionary optimizer is used to further enhance the simultaneous visualization of solutions in objective and parameter space. Two methods for visualizing multiobjective solutions in the plane are introduced. First, we use RadViz and exploit interpretations of barycentric coordinates for convex polygons and simplices to map a mutually nondominating set to the interior of a regular convex polygon in the plane, providing an intuitive representation of the solutions and objectives. Second, we introduce a new measure of the similarity of solutions—the dominance distance—which captures the order relations between solutions. This metric provides an embedding in Euclidean space, which is shown to yield coherent visualizations in two dimensions. The methods are illustrated on standard test problems and data from a benchmark many-objective problem

Visualising many-objective populations

Copyright © 2012 ACM14th International Conference on Genetic and Evolutionary Computation (GECCO 2012), Philadelphia, USA, 7-11 July 2012Optimisation problems often comprise a large set of objectives, and visualising the set of solutions to a problem can help with understanding them, assisting a decision maker. If the set of objectives is larger than three, visualising solutions to the problem is a difficult task. Techniques for visualising high-dimensional data are often difficult to interpret. Conversely, discarding objectives so that the solutions can be visualised in two or three spatial dimensions results in a loss of potentially important information. We demonstrate four methods for visualising many-objective populations, two of which use the complete set of objectives to present solutions in a clear and intuitive fashion and two that compress the objectives of a population into two dimensions whilst minimising the information that is lost. All of the techniques are illustrated on populations of solutions to optimisation test problems