Population diversity is essential for avoiding premature convergence in genetic algorithms
(GAs) and for the effective use of crossover. Yet the dynamics of how diversity emerges in …

One of the most common questions I am asked when introducing people to the Enneagram
goes something along the lines of:“Yes, but is it real? What scientific evidence is there?” It …

Practical optimization problems frequently include uncertainty about the quality measure, for
example, due to noisy evaluations. Thus, they do not allow for a straightforward application …

This chapter first reviews the simple genetic algorithm. Mathematical models of the genetic
algorithm are also reviewed, including the schema theorem, exact infinite population …

Recent results show that the Differential Evolution algorithm has significant difficulty on
functions that are not linearly separable. On such functions, the algorithm must rely primarily …

Population diversity is essential for the effective use of any crossover operator. We compare
seven commonly used diversity mechanisms and prove rigorous run time bounds for the (μ+ …

Jump functions were originally introduced as benchmarks on which recombinant
evolutionary algorithms can provably outperform those that use mutation alone. To optimize …

Bit-flip mutation is a common mutation operator for evolutionary algorithms applied to
optimize functions over binary strings. In this paper, we develop results from the theory of …

Hill climbing algorithms are at the core of many approaches to solve optimization problems.
Such algorithms usually require the complete enumeration of a neighborhood of the current …

Four preterm infants of very low birthweight (less than 1500 g) developed signs of copper
deficiency between age 8 and 10 weeks. All had required prolonged ventilatory support …