Evolution algorithms are stochastic optimization 
methods based on evolutionary principles. They 
have long been used in optimization, and are 
gaining in popularity. They are particularly 
useful in high dimensional problems, or in problems 
where gradient methods fails. Evolution strategies, 
a class of evolutionary algorithms, are stochastic 
searches which evolve by "mutation". 
This work proposes a new mutation distribution for 
use in single objective optimization. Up to now, 
cost function information obtained by mutations that 
do not improve fitness has been discarded. In many 
problems, particularly when cost function calls are 
expensive, it is desirable to use all available 
information to guide the search. The new method in 
this work patches Gaussians of different variances 
together to create a sampling distribution which 
delivers mutations designed to direct the search away 
from regions where low values of fitness have been 
observed. Analytic results for this new method are 
derived on idealized problems. The method is compared 
with existing methods on a range of test problems, and 
its overall performance attributes are assessed. A new 
method for multiobjective optimization is also developed. 
Genetic Algorithms introduce innovation into their 
populations by a process of bit mutation. This small 
scale mutation is often insufficient to successfully 
direct the search, unless the initial population is of 
sufficient quality. The new method proposed here, termed 
"Rank Biased Sampling", uses the population to create new 
members, which are resampled across the entire search space 
from a distribution designed to favor regions which are 
inadequately represented by the current population. Again, 
this method is compared to existing methods on some standard 
test problems. These new optimization methods are then 
applied to some real-world problems of engineering interest. 
The optimization routines developed in this work performed 
well on these applications, and provide good improvements on 
existing methods, as well as opening up avenues for further 
research.