Jiří Kubalík

Software project portfolio optimization with advanced multiobjective evolutionary algorithms

Large software companies have to plan their project portfolio to maximize potential portfolio return and strategic alignment, while balancing various preferences, and considering limited resources. Project portfolio managers need methods and tools to find a good solution for complex project portfolios and multiobjective target criteria efficiently. However, software project portfolios are challenging to describe for optimization in a practical way that allows efficient optimization. In this paper we propose an approach to describe software project portfolios with a set of multiobjective criteria for portfolio managers using the COCOMO II model and introduce a multiobjective evolutionary approach, mPOEMS, to find the Pareto-optimal front efficiently. We evaluate the new approach with portfolios choosing from a set of 50 projects that follow the validated COCOMO II model criteria and compare the performance of the mPOEMS approach with state-of-the-art multiobjective optimization evolutionary approaches. Major results are as follows: the portfolio management approach was found usable and useful; the mPOEMS approach outperformed the other approaches.

Hyper-Heuristic based on iterated local search driven by evolutionary algorithm

This paper proposes an evolutionary-based iterative local search hyper-heuristic approach called Iterated Search Driven by Evolutionary Algorithm Hyper-Heuristic (ISEA). Two versions of this algorithm, ISEA-chesc and ISEA-adaptive, that differ in the re-initialization scheme are presented. The performance of the two algorithms was experimentally evaluated on six hard optimization problems using the HyFlex experimental framework and the algorithms were compared with algorithms that took part in the CHeSC 2011 challenge. Achieved results are very promising, the ISEA-adaptive would take the second place in the competition. It shows how important for good performance of this iterated local search hyper-heuristic is the re-initialization strategy.

Solving the DNA fragment assembly problem efficiently using iterative optimization with evolved hypermutations

The paper presents a successful application of an evolutionary based iterative optimization method called Prototype Optimization with Evolved Improvement Steps (POEMS) to the DNA fragment assembly problem. The DNA fragment assembly problem, known to be NP-hard, is of great importance as it constitutes an important step in the genome project. The POEMS is an iterative algorithm that employs an evolutionary algorithm for exploration of the current solutions neighborhood in each iteration of the optimization process. Experiments show that the proposed POEMS approach performs very well and generates better results than those generated by other state-of-the-art methods.

Solving the sorting network problem using iterative optimization with evolved hypermutations

This paper presents an application of a prototype optimization with evolved improvement steps algorithm (POEMS) to the well-known problem of optimal sorting network design. The POEMS is an iterative algorithm that seeks the best variation of the current solution in each iteration. The variations, also called hypermutations, are evolved by means of an evolutionary algorithm. We compared the POEMS to two mutation-based optimizers, namely the (\mu+\lambda)- and (1+\lambda)-evolution strategies. For experimental evaluation 10-input, 12-input, 14-input and 16-input instances of the sorting network problem were used. Results show that the proposed POEMS approach clearly outperforms both compared algorithms. Moreover, POEMS was able to find several perfect networks that are equivalent w.r.t. the number of comparators to the best known solutions for the 10-input, 12-input, 14-input, and 16-input problems. Finally, we propose a modification to the POEMS approach that might further improve its performance.

Genetic algorithms and their testing

Genetic Algorithms (GAs) are adaptive search methods, which try to incorporate the principle of surviving known from nature. They proved to be an efficient instrument for solving many hard problems in different areas, in which the majority of other techniques failed as being weak or not applicable. On the other hand, GAs fight with a number of problems, as well. To the crucial issues belong the representation of potential solutions in the search space, design of the proper operators that drive the search and the configuration of the routines and strategies used in the GAs. This paper presents some results of our research on GAs. The interesting observations encountered by experiments concerning the initialisation of GAs’ runs and by the enhanced crossover operator for binary chromosomes are presented. We have used a shell called GATool for automatic experimenting with GAs, which was developed for GAs’ performance evaluation. The representatives of Travelling Salesman Problem (TSP), Continuous functions an...

Efficient stochastic local search algorithm for solving the shortest common supersequence problem

The Shortest Common Supersequence (SCS) problem is a well-known hard combinatorial optimization problem that formalizes many real world problems. Recently, an application of the iterative optimization method called Prototype Optimization with Evolved Improvement Steps (POEMS) to the SCS problem has been proposed. The POEMS seeks the best variation of the current solution in each iteration. The variations, considered as structured hypermutations, are evolved by means of an evolutionary algorithm. This approach has been shown to work very well on synthetic as well as real biological data. However, the approach exhibited rather low scalability which is caused by very time demanding evaluation function. This paper proposes a new time efficient evaluation procedure and a new moving-window strategy for constructing and refining the supersequence. These two enhancements significantly improve an efficiency of the approach. Series of experiments with the modified POEMS method have been carried out. Results presented in this paper show that the method is competitive with current state-of-the-art algorithms for solving the SCS problem. Moreover, there is a potential for further improvement as discussed in the conclusions.

Solving the multiple sequence alignment problem using prototype optimization with evolved improvement steps

This paper deals with a Multiple Sequence Alignment problem, for which an implementation of the Prototype Optimization with Evolved Improvement Steps (POEMS) algorithm has been proposed. The key feature of the POEMS is that it takes some initial solution, which is then iteratively improved by means of what we call evolved hypermutations. In this work, the POEMS is seeded with a solution provided by the Clustal X algorithm. Major result of the presented experiments was that the proposed POEMS implementation performs significantly better than the other two compared algorithms, which rely on randomhypermutations only. Based on the carried out analyses we proposed two modifications of the POEMS algorithm that might further improve its performance.

Alarm Root Cause Detection System

Production process control becomes complicated as the complexity of the controlled process grows. To simplify the operator’s role many computer based control systems with integrated visualization clients have been developed. In many practical circumstances malfunction of one or more process components results in other related components entering the alarm states. Several alarms appear on the operator’s display in a short time making it difficult for the operator to diagnose the root cause quickly. Within this paper we describe a solution of this problem based on a multi-agent system that processes all incoming alarms, identifies the root cause alarms vs. alarms arising in consequence of the roots and presents the diagnostic results to the operator visually.

Co-evolutionary approach to design of robotic gait

Manual design of motion patterns for legged robots is difficult task often with suboptimal results. To automate this process variety of approaches have been tried including various evolutionary algorithms. In this work we present an algorithm capable of generating viable motion patterns for multi-legged robots. This algorithm consists of two evolutionary algorithms working in co-evolution. The GP is evolving motion of a single leg while the GA deploys the motion to all legs of the robot. Proof-of-concept experiments show that the co-evolutionary approach delivers significantly better results than those evolved for the same robot with simple genetic programming algorithm alone.

Black-box optimization benchmarking of prototype optimization with evolved improvement steps for noiseless function testbed

This paper presents benchmarking of a stochastic local search algorithm called Prototype Optimization with Evolved Improvement Steps (POEMS) on the noise-free BBOB 2009 testbed. Experiments for 2, 3, 5, 10 and 20 D were done, where D denotes the search space dimension. The maximum number of function evaluations is chosen as 105 x D. Experimental results show that POEMS performs best on all separable functions and the attractive sector function. It works also quite well on multi-modal functions with lower dimensions. On the other hand, the algorithm fails to solve functions with high conditioning.