Automated Innovization: Knowledge discovery through multi-objective optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Sunith Bandaru",
  title =        "Automated Innovization: Knowledge discovery through
                 multi-objective optimization",
  school =       "Indian Institute of Technology Kanpur",
  year =         "2013",
  address =      "India",
  keywords =     "genetic algorithms, genetic programming, Innovization,
  URL =          "",
  URL =          "",
  size =         "227 pages",
  abstract =     "In recent years, there has been a growing interest in
                 the field of post-optimality analysis. In a single
                 objective scenario, this usually concerns optimality,
                 sensitivity and robustness studies on the obtained
                 solution. Multi-objective optimization on the other
                 hand, poses an additional challenge in that there are a
                 multitude of possible solutions (when the objectives
                 are conflicting) which are all said to be
                 Pareto-optimal. These solutions may collectively hold
                 crucial design information. Properties that are common
                 to all or most Pareto-optimal solutions can be
                 considered as characteristic features that make good
                 designs. Knowledge of such features, in addition to
                 providing better insights into the problem at hand,
                 enables the designer to hand craft solutions for other
                 optimization tasks which are structurally similar to
                 it; thus eliminating the need to actually optimize.
                 Innovization is the process of extracting this
                 information in the form of design principles, which are
                 basically mathematical expressions relating various
                 problem entities like variables, objectives and
                 constraint functions. Manual innovization involves the
                 visual identification of correlations between problem
                 entities through two and three-dimensional plots.
                 Thereafter, appropriate functions are used for
                 regression and the design principles are obtained.
                 Though the procedure has been applied to many
                 engineering design problems, the human element involved
                 in it limits its use in most practical

                 The present thesis firstly proposes automated
                 innovization, an unsupervised machine learning
                 technique that can identify correlations in any
                 multi-dimensional space formed by variables,
                 objectives, etc. specified by the user and subsequently
                 performs a selective regression on the correlated part
                 of the Pareto-optimal dataset to obtain a design
                 principle. The correlations are automatically
                 identified by a customized grid-based clustering
                 algorithm and the design principle is evolved using a
                 genetic algorithm. Next, the procedure is extended so
                 that design principles hidden in all possible Euclidean
                 spaces formed by the variables and objectives (and any
                 other user-defined functions) can be obtained
                 simultaneously, without any human interaction, in a
                 single run of the algorithm. This is accomplished by
                 introducing a niching strategy to evolve different
                 ‘species’ of design principles in the same
                 population of a genetic algorithm. Automation in
                 innovization is achieved at the cost of restricting the
                 mathematical structure of the design principles to a
                 certain form, the significance of which becomes clear
                 by observing physical laws in nature. Later in this
                 thesis, a tree-based genetic programming framework is
                 integrated into automated innovization to obtain design
                 principles of any generic mathematical structure.
                 Dimensionality information is introduced in the search
                 process to produce design principles that are
                 meaningful to the designer.

                 Next, the proposed automated innovization technique is
                 used to obtain design principles for four real-world
                 multi-objective design optimization problems from
                 varied fields. They are: noise barrier design
                 optimization, polymer extrusion process optimization,
                 friction stir welding process optimization and MEMS
                 (MicroElectroMechanical Systems) resonator design
                 optimization. In each case the obtained design
                 principles are presented to experts of the respective
                 fields for interpretation to gain insights.

                 Secondly, this thesis introduces two new innovization
                 concepts, namely higher-level innovization and
                 lower-level innovization. Multi-objective optimization
                 problem formulations involve many settings that are not
                 changed during the solution process. However, once the
                 trade-off front is generated, the designer may wish to
                 change them and rerun the optimization, thus obtaining
                 more fronts. This happens in many real-world situations
                 where the designer is initially unsure about problem
                 elements such as constraints, variable bounds,
                 parameters and even objective functions. Higher-level
                 innovization answers questions like: ‘Are the
                 features of the original problem still valid for other
                 generated fronts? If not, how do they change with the
                 modified setting?’. The name reflects the fact that
                 higher-level design knowledge is gained in the process.
                 Sometimes lower-level design knowledge may also be
                 desired. Consider the situation when after obtaining a
                 set of trade-off solutions for a multi-objective design
                 problem, a posteriori decision-making approach is used
                 to identify a region of preference on the trade-off
                 front. Now the designer may be interested in knowing
                 features that are common to solutions only in this
                 partial set and are not seen in rest of the trade-off
                 solutions, so that the designer is specifically aware
                 of properties associated with the chosen solutions. In
                 this thesis, the automated innovization technique is
                 extended to perform both higher and lower-level

                 Thirdly, this thesis studies the temporal evolution of
                 design principles obtained using automated innovization
                 during the course of optimization. Results on a few
                 engineering design problems reveal that certain
                 important design features start to evolve early on,
                 whereas some detailed design features appear later
                 during optimization. Interestingly, there exists a
                 simile between evolution of design principles in
                 engineering and human evolution. Such information about
                 the hierarchy of key design principles should enable
                 designers to have a deeper understanding of their
  notes =        "SunithBandaru_PhDThesis.pdf",

Genetic Programming entries for Sunith Bandaru