Together with the NSGA-II, the SPEA2 is one of the most widely used domination-based multi-objective evolutionary algorithms. For both algorithms, the known runtime guarantees are linear in the population size; for the NSGA-II, matching lower bounds exist. With a careful study of the more complex selection mechanism of the SPEA2, we show that it has very different population dynamics. From these, we prove runtime guarantees for the OneMinMax, LeadingOnesTrailingZeros, and OneJumpZeroJump benchmarks that depend less on the population size. For example, we show that the SPEA2 with parent population size mu >= n - 2k + 3 and offspring population size lambda computes the Pareto front of the OneJumpZeroJump benchmark with gap size k in an expected number of O((lambda+mu)n + n^(k+1)) function evaluations. This shows that the best runtime guarantee of O(n^(k+1)) is not only achieved for mu = Theta(n) and lambda = O(n) but for arbitrary mu, lambda = O(n^k). Thus, choosing suitable parameters - a key challenge in using heuristic algorithms - is much easier for the SPEA2 than the NSGA-II.

In this paper we propose an alternative method for generating topic suggestions to be used in an Open Innovation based expert identification system. An important requirement within this challenge is the identification of topics lateral to a given innovation problem, and use them to broaden the broadcast of the problem without compromising on relevancy. We propose an approach based on DBPedia, using which we can recommend topics on certain proximity in the DBPedia concept graph. We show the important impact of the use of suggested lateral keywords to the raised awareness about the problem in a real problem broadcast.

In this paper we explore the problems of dedicating status messages to their intended audience on the Web. We present the results of our qualitative user study aimed at revealing the nature of the problem. We then present the existing solutions for directing status messages to their audience and con